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overflow: hidden; text-overflow: ellipsis; -webkit-line-clamp: 3; -webkit-box-orient: vertical; }</style><div class="col-xs-12 clearfix"><div class="u-floatLeft"><h1 class="PageHeader-title u-m0x u-fs30">Bayesian Probability</h1><div class="u-tcGrayDark">162 Followers</div><div class="u-tcGrayDark u-mt2x">Recent papers in <b>Bayesian Probability</b></div></div></div></div></div></div><div class="TabbedNavigation"><div class="container"><div class="row"><div class="col-xs-12 clearfix"><ul class="nav u-m0x u-p0x list-inline u-displayFlex"><li class="active"><a href="https://www.academia.edu/Documents/in/Bayesian_Probability">Top Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Bayesian_Probability/MostCited">Most Cited Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Bayesian_Probability/MostDownloaded">Most Downloaded Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Bayesian_Probability/MostRecent">Newest Papers</a></li><li><a class="" href="https://www.academia.edu/People/Bayesian_Probability">People</a></li></ul></div><style type="text/css">ul.nav{flex-direction:row}@media(max-width: 567px){ul.nav{flex-direction:column}.TabbedNavigation li{max-width:100%}.TabbedNavigation li.active{background-color:var(--background-grey, #dddde2)}.TabbedNavigation li.active:before,.TabbedNavigation li.active:after{display:none}}</style></div></div></div><div class="container"><div class="row"><div class="col-xs-12"><div class="u-displayFlex"><div class="u-flexGrow1"><div class="works"><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_75273766" data-work_id="75273766" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/75273766/Bayesian_Posterior_Predictive_Probability_Happiness">Bayesian Posterior Predictive Probability Happiness</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We propose to determine the underlying causal structure of the elements of happiness from a set of empirically obtained data based on Bayesian. We consider the proposal to study happiness as a multidimensional construct which converges... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_75273766" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We propose to determine the underlying causal structure of the elements of happiness from a set of empirically obtained data based on Bayesian. We consider the proposal to study happiness as a multidimensional construct which converges four dimensions with two different Bayesian techniques, in the first we use the Bonferroni correction to estimate the mean multiple comparisons, on this basis it is that we use the function t and a z-test, in both cases the results do not vary, so it is decided to present only those shown by the t test. In the Bayesian Multiple Linear Regression, we prove that happiness can be explained through three dimensions. The technical numerical used is MCMC, of four samples. The results show that the sample has not atypical behavior too and that suitable modifications can be described through a test. Another interesting result obtained is that the predictive probability for the case of sense positive of life and personal fulfillment dimensions exhibit a non-uniform variation.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/75273766" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4ca1529e6f9c473d95d0d6bc7fd56f90" rel="nofollow" data-download="{"attachment_id":83108818,"asset_id":75273766,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/83108818/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="79263047" href="https://uptlax.academia.edu/GabyRodriguez">Gaby Rodriguez</a><script data-card-contents-for-user="79263047" type="text/json">{"id":79263047,"first_name":"Gaby","last_name":"Rodriguez","domain_name":"uptlax","page_name":"GabyRodriguez","display_name":"Gaby Rodriguez","profile_url":"https://uptlax.academia.edu/GabyRodriguez?f_ri=1032783","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_75273766 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="75273766"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 75273766, container: ".js-paper-rank-work_75273766", }); });</script></li><li class="js-percentile-work_75273766 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75273766; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_75273766"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_75273766 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="75273766"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75273766; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75273766]").text(description); $(".js-view-count-work_75273766").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_75273766").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="75273766"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="305" rel="nofollow" href="https://www.academia.edu/Documents/in/Applied_Mathematics">Applied Mathematics</a>, <script data-card-contents-for-ri="305" type="text/json">{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="892" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistics">Statistics</a>, <script data-card-contents-for-ri="892" type="text/json">{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4390" rel="nofollow" href="https://www.academia.edu/Documents/in/MCMC">MCMC</a>, <script data-card-contents-for-ri="4390" type="text/json">{"id":4390,"name":"MCMC","url":"https://www.academia.edu/Documents/in/MCMC?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14818" rel="nofollow" href="https://www.academia.edu/Documents/in/Happiness">Happiness</a><script data-card-contents-for-ri="14818" type="text/json">{"id":14818,"name":"Happiness","url":"https://www.academia.edu/Documents/in/Happiness?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=75273766]'), work: {"id":75273766,"title":"Bayesian Posterior Predictive Probability Happiness","created_at":"2022-04-02T20:41:07.071-07:00","url":"https://www.academia.edu/75273766/Bayesian_Posterior_Predictive_Probability_Happiness?f_ri=1032783","dom_id":"work_75273766","summary":"We propose to determine the underlying causal structure of the elements of happiness from a set of empirically obtained data based on Bayesian. We consider the proposal to study happiness as a multidimensional construct which converges four dimensions with two different Bayesian techniques, in the first we use the Bonferroni correction to estimate the mean multiple comparisons, on this basis it is that we use the function t and a z-test, in both cases the results do not vary, so it is decided to present only those shown by the t test. In the Bayesian Multiple Linear Regression, we prove that happiness can be explained through three dimensions. The technical numerical used is MCMC, of four samples. The results show that the sample has not atypical behavior too and that suitable modifications can be described through a test. Another interesting result obtained is that the predictive probability for the case of sense positive of life and personal fulfillment dimensions exhibit a non-uniform variation.","downloadable_attachments":[{"id":83108818,"asset_id":75273766,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":79263047,"first_name":"Gaby","last_name":"Rodriguez","domain_name":"uptlax","page_name":"GabyRodriguez","display_name":"Gaby Rodriguez","profile_url":"https://uptlax.academia.edu/GabyRodriguez?f_ri=1032783","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics?f_ri=1032783","nofollow":true},{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1032783","nofollow":true},{"id":4390,"name":"MCMC","url":"https://www.academia.edu/Documents/in/MCMC?f_ri=1032783","nofollow":true},{"id":14818,"name":"Happiness","url":"https://www.academia.edu/Documents/in/Happiness?f_ri=1032783","nofollow":true},{"id":36621,"name":"Happiness and Well Being","url":"https://www.academia.edu/Documents/in/Happiness_and_Well_Being?f_ri=1032783"},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_80245073" data-work_id="80245073" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/80245073/Bayesian_Network_Model_to_Distinguish_COVID_19_for_Illness_with_Similar_Symptoms">Bayesian Network Model to Distinguish COVID-19 for Illness with Similar Symptoms</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Numerous diseases and illnesses exhibit similar physical and medical symptoms, such as COVID-19 and its similar disguised illness (common cold, flu, and seasonal allergies). In this study, we construct a Bayesian Network model to... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_80245073" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Numerous diseases and illnesses exhibit similar physical and medical symptoms, such as COVID-19 and its similar disguised illness (common cold, flu, and seasonal allergies). In this study, we construct a Bayesian Network model to distinguish such symptom variables in a classification task. The Bayesian Network model has been widely used as a classifier comparable to machine learning models. We develop the model with a scoring-based method and implement it using a hill-climbing algorithm with the Bayesian information criterion (BIC) score approach. Experimental evaluations using publicly available Mayo Clinic based data using this Bayesian Network model that present Directed Acyclic Graph (DAG) which can show the relationship between the similar symptoms and the type of disease with Conditional Probability Table (CPT). This model shows a promising accuracy performance up to 93.14% which is better than the performance of other machine learning classifiers, including the Support Vector...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/80245073" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="5bada663deac610c1885667c31ede07d" rel="nofollow" data-download="{"attachment_id":86691220,"asset_id":80245073,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/86691220/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="96172836" href="https://independent.academia.edu/EmirLuthfi">Emir Luthfi</a><script data-card-contents-for-user="96172836" type="text/json">{"id":96172836,"first_name":"Emir","last_name":"Luthfi","domain_name":"independent","page_name":"EmirLuthfi","display_name":"Emir Luthfi","profile_url":"https://independent.academia.edu/EmirLuthfi?f_ri=1032783","photo":"https://0.academia-photos.com/96172836/22184589/21414352/s65_emir.luthfi.jpg"}</script></span></span></li><li class="js-paper-rank-work_80245073 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="80245073"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 80245073, container: ".js-paper-rank-work_80245073", }); });</script></li><li class="js-percentile-work_80245073 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80245073; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_80245073"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_80245073 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="80245073"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80245073; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80245073]").text(description); $(".js-view-count-work_80245073").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_80245073").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="80245073"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">10</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="465" rel="nofollow" href="https://www.academia.edu/Documents/in/Artificial_Intelligence">Artificial Intelligence</a>, <script data-card-contents-for-ri="465" type="text/json">{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="70995" rel="nofollow" href="https://www.academia.edu/Documents/in/Random_Forest">Random Forest</a><script data-card-contents-for-ri="70995" type="text/json">{"id":70995,"name":"Random Forest","url":"https://www.academia.edu/Documents/in/Random_Forest?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=80245073]'), work: {"id":80245073,"title":"Bayesian Network Model to Distinguish COVID-19 for Illness with Similar Symptoms","created_at":"2022-05-30T00:27:58.852-07:00","url":"https://www.academia.edu/80245073/Bayesian_Network_Model_to_Distinguish_COVID_19_for_Illness_with_Similar_Symptoms?f_ri=1032783","dom_id":"work_80245073","summary":"Numerous diseases and illnesses exhibit similar physical and medical symptoms, such as COVID-19 and its similar disguised illness (common cold, flu, and seasonal allergies). In this study, we construct a Bayesian Network model to distinguish such symptom variables in a classification task. The Bayesian Network model has been widely used as a classifier comparable to machine learning models. We develop the model with a scoring-based method and implement it using a hill-climbing algorithm with the Bayesian information criterion (BIC) score approach. Experimental evaluations using publicly available Mayo Clinic based data using this Bayesian Network model that present Directed Acyclic Graph (DAG) which can show the relationship between the similar symptoms and the type of disease with Conditional Probability Table (CPT). This model shows a promising accuracy performance up to 93.14% which is better than the performance of other machine learning classifiers, including the Support Vector...","downloadable_attachments":[{"id":86691220,"asset_id":80245073,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":96172836,"first_name":"Emir","last_name":"Luthfi","domain_name":"independent","page_name":"EmirLuthfi","display_name":"Emir Luthfi","profile_url":"https://independent.academia.edu/EmirLuthfi?f_ri=1032783","photo":"https://0.academia-photos.com/96172836/22184589/21414352/s65_emir.luthfi.jpg"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=1032783","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1032783","nofollow":true},{"id":70995,"name":"Random Forest","url":"https://www.academia.edu/Documents/in/Random_Forest?f_ri=1032783","nofollow":true},{"id":191289,"name":"Support vector machine","url":"https://www.academia.edu/Documents/in/Support_vector_machine?f_ri=1032783"},{"id":274599,"name":"Bayesian Network","url":"https://www.academia.edu/Documents/in/Bayesian_Network?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network?f_ri=1032783"},{"id":1553450,"name":"Naive Bayes Classifier","url":"https://www.academia.edu/Documents/in/Naive_Bayes_Classifier?f_ri=1032783"},{"id":2143462,"name":"Directed Acyclic Graph","url":"https://www.academia.edu/Documents/in/Directed_Acyclic_Graph?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_76926023" data-work_id="76926023" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/76926023/Bayesian_models_for_prediction_of_the_set_difference_in_volleyball">Bayesian models for prediction of the set-difference in volleyball</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The aim of this paper is to study and develop Bayesian models for the analysis of volleyball match outcomes as recorded by the set-difference. Due to the peculiarity of the outcome variable (set-difference) which takes discrete values... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_76926023" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The aim of this paper is to study and develop Bayesian models for the analysis of volleyball match outcomes as recorded by the set-difference. Due to the peculiarity of the outcome variable (set-difference) which takes discrete values from $-3$ to $3$, we cannot consider standard models based on the usual Poisson or binomial assumptions used for other sports such as football/soccer. Hence, the first and foremost challenge was to build models appropriate for the set-differences of each volleyball match. Here we consider two major approaches: a) an ordered multinomial logistic regression model and b) a model based on a truncated version of the Skellam distribution. For the first model, we consider the set-difference as an ordinal response variable within the framework of multinomial logistic regression models. Concerning the second model, we adjust the Skellam distribution in order to account for the volleyball rules. We fit and compare both models with the same covariate structure as...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/76926023" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="00fc302ec8b82105687f03ba31df3962" rel="nofollow" data-download="{"attachment_id":84454636,"asset_id":76926023,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/84454636/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="116424713" href="https://independent.academia.edu/SotirisDrikos">Sotiris Drikos</a><script data-card-contents-for-user="116424713" type="text/json">{"id":116424713,"first_name":"Sotiris","last_name":"Drikos","domain_name":"independent","page_name":"SotirisDrikos","display_name":"Sotiris Drikos","profile_url":"https://independent.academia.edu/SotirisDrikos?f_ri=1032783","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_76926023 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="76926023"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 76926023, container: ".js-paper-rank-work_76926023", }); });</script></li><li class="js-percentile-work_76926023 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 76926023; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_76926023"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_76926023 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="76926023"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76926023; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=76926023]").text(description); $(".js-view-count-work_76926023").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_76926023").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="76926023"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="300" rel="nofollow" href="https://www.academia.edu/Documents/in/Mathematics">Mathematics</a>, <script data-card-contents-for-ri="300" type="text/json">{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="41239" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_statistics_and_modelling">Bayesian statistics & modelling</a>, <script data-card-contents-for-ri="41239" type="text/json">{"id":41239,"name":"Bayesian statistics \u0026 modelling","url":"https://www.academia.edu/Documents/in/Bayesian_statistics_and_modelling?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="226632" rel="nofollow" href="https://www.academia.edu/Documents/in/Sports_analytics">Sports analytics</a><script data-card-contents-for-ri="226632" type="text/json">{"id":226632,"name":"Sports analytics","url":"https://www.academia.edu/Documents/in/Sports_analytics?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=76926023]'), work: {"id":76926023,"title":"Bayesian models for prediction of the set-difference in volleyball","created_at":"2022-04-19T02:13:27.649-07:00","url":"https://www.academia.edu/76926023/Bayesian_models_for_prediction_of_the_set_difference_in_volleyball?f_ri=1032783","dom_id":"work_76926023","summary":"The aim of this paper is to study and develop Bayesian models for the analysis of volleyball match outcomes as recorded by the set-difference. Due to the peculiarity of the outcome variable (set-difference) which takes discrete values from $-3$ to $3$, we cannot consider standard models based on the usual Poisson or binomial assumptions used for other sports such as football/soccer. Hence, the first and foremost challenge was to build models appropriate for the set-differences of each volleyball match. Here we consider two major approaches: a) an ordered multinomial logistic regression model and b) a model based on a truncated version of the Skellam distribution. For the first model, we consider the set-difference as an ordinal response variable within the framework of multinomial logistic regression models. Concerning the second model, we adjust the Skellam distribution in order to account for the volleyball rules. We fit and compare both models with the same covariate structure as...","downloadable_attachments":[{"id":84454636,"asset_id":76926023,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":116424713,"first_name":"Sotiris","last_name":"Drikos","domain_name":"independent","page_name":"SotirisDrikos","display_name":"Sotiris Drikos","profile_url":"https://independent.academia.edu/SotirisDrikos?f_ri=1032783","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=1032783","nofollow":true},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true},{"id":41239,"name":"Bayesian statistics \u0026 modelling","url":"https://www.academia.edu/Documents/in/Bayesian_statistics_and_modelling?f_ri=1032783","nofollow":true},{"id":226632,"name":"Sports analytics","url":"https://www.academia.edu/Documents/in/Sports_analytics?f_ri=1032783","nofollow":true},{"id":630894,"name":"Sports Performance Analysis","url":"https://www.academia.edu/Documents/in/Sports_Performance_Analysis?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_19519878" data-work_id="19519878" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/19519878/Adaptive_Traffic_Control_System_based_on_Bayesian_Probability_Interpretation">Adaptive Traffic Control System based on Bayesian Probability Interpretation</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Traffic control (TC) is a challenging problem in today’s modern society. This is due to several factors including the huge number of vehicles, the high dynamics of the system, and the nonlinear behavior exhibited by the different... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_19519878" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Traffic control (TC) is a challenging problem in today’s modern society. This is due to several factors including the huge number of vehicles, the high dynamics of the system, and the nonlinear behavior exhibited by the different components of the system. Poor traffic management inflicts considerable cost due to the high rate of accidents, time losses, and negative impact on the economy as well as the environment. In this paper, we develop a traffic control system based on the Bayesian interpretation of probability that is adaptive to the high dynamics and non-stationarity of the road network. In order to simulate the traffic non-stationarity, we extend the Green Light District (GLD) vehicle traffic simulator. The change in road conditions is modeled by varying vehicle spawning probability distributions. We also implement the acceleration and lane changing models in GLD based on the Intelligent Driver Model (IDM).<br />Index Terms—reinforcement learning, traffic control, traffic simulation, multi agent systems, driver behavior</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/19519878" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="825b576f5d149e345c354e3d5b5db231" rel="nofollow" data-download="{"attachment_id":40666411,"asset_id":19519878,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/40666411/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="37877592" href="https://ejust.academia.edu/walid_gomaa">Walid Gomaa</a><script data-card-contents-for-user="37877592" type="text/json">{"id":37877592,"first_name":"Walid","last_name":"Gomaa","domain_name":"ejust","page_name":"walid_gomaa","display_name":"Walid Gomaa","profile_url":"https://ejust.academia.edu/walid_gomaa?f_ri=1032783","photo":"https://0.academia-photos.com/37877592/10632393/11942071/s65_walid.gomaa.png"}</script></span></span></li><li class="js-paper-rank-work_19519878 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="19519878"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 19519878, container: ".js-paper-rank-work_19519878", }); 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$(".js-view-count[data-work-id=19519878]").text(description); $(".js-view-count-work_19519878").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_19519878").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="19519878"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">15</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="1688" rel="nofollow" href="https://www.academia.edu/Documents/in/Reinforcement_Learning">Reinforcement Learning</a>, <script data-card-contents-for-ri="1688" type="text/json">{"id":1688,"name":"Reinforcement Learning","url":"https://www.academia.edu/Documents/in/Reinforcement_Learning?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2534" rel="nofollow" href="https://www.academia.edu/Documents/in/Multiagent_Systems">Multiagent Systems</a>, <script data-card-contents-for-ri="2534" type="text/json">{"id":2534,"name":"Multiagent Systems","url":"https://www.academia.edu/Documents/in/Multiagent_Systems?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="3252" rel="nofollow" href="https://www.academia.edu/Documents/in/Traffic_Simulation">Traffic Simulation</a><script data-card-contents-for-ri="3252" type="text/json">{"id":3252,"name":"Traffic Simulation","url":"https://www.academia.edu/Documents/in/Traffic_Simulation?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=19519878]'), work: {"id":19519878,"title":"Adaptive Traffic Control System based on Bayesian Probability Interpretation","created_at":"2015-12-06T06:11:35.678-08:00","url":"https://www.academia.edu/19519878/Adaptive_Traffic_Control_System_based_on_Bayesian_Probability_Interpretation?f_ri=1032783","dom_id":"work_19519878","summary":"Traffic control (TC) is a challenging problem in today’s modern society. This is due to several factors including the huge number of vehicles, the high dynamics of the system, and the nonlinear behavior exhibited by the different components of the system. Poor traffic management inflicts considerable cost due to the high rate of accidents, time losses, and negative impact on the economy as well as the environment. In this paper, we develop a traffic control system based on the Bayesian interpretation of probability that is adaptive to the high dynamics and non-stationarity of the road network. In order to simulate the traffic non-stationarity, we extend the Green Light District (GLD) vehicle traffic simulator. The change in road conditions is modeled by varying vehicle spawning probability distributions. We also implement the acceleration and lane changing models in GLD based on the Intelligent Driver Model (IDM).\nIndex Terms—reinforcement learning, traffic control, traffic simulation, multi agent systems, driver behavior","downloadable_attachments":[{"id":40666411,"asset_id":19519878,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":37877592,"first_name":"Walid","last_name":"Gomaa","domain_name":"ejust","page_name":"walid_gomaa","display_name":"Walid Gomaa","profile_url":"https://ejust.academia.edu/walid_gomaa?f_ri=1032783","photo":"https://0.academia-photos.com/37877592/10632393/11942071/s65_walid.gomaa.png"}],"research_interests":[{"id":1688,"name":"Reinforcement Learning","url":"https://www.academia.edu/Documents/in/Reinforcement_Learning?f_ri=1032783","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1032783","nofollow":true},{"id":2534,"name":"Multiagent Systems","url":"https://www.academia.edu/Documents/in/Multiagent_Systems?f_ri=1032783","nofollow":true},{"id":3252,"name":"Traffic Simulation","url":"https://www.academia.edu/Documents/in/Traffic_Simulation?f_ri=1032783","nofollow":true},{"id":15084,"name":"Statistical machine learning","url":"https://www.academia.edu/Documents/in/Statistical_machine_learning?f_ri=1032783"},{"id":22247,"name":"Traffic control","url":"https://www.academia.edu/Documents/in/Traffic_control?f_ri=1032783"},{"id":26813,"name":"MultiAgent Systems (Computer Science)","url":"https://www.academia.edu/Documents/in/MultiAgent_Systems_Computer_Science_?f_ri=1032783"},{"id":27224,"name":"Multiagent Systems (Intelligence)","url":"https://www.academia.edu/Documents/in/Multiagent_Systems_Intelligence_?f_ri=1032783"},{"id":28846,"name":"Bayesian Probabilistic Analysis","url":"https://www.academia.edu/Documents/in/Bayesian_Probabilistic_Analysis?f_ri=1032783"},{"id":103411,"name":"Traffic management and control","url":"https://www.academia.edu/Documents/in/Traffic_management_and_control?f_ri=1032783"},{"id":193953,"name":"Driver Behavior","url":"https://www.academia.edu/Documents/in/Driver_Behavior?f_ri=1032783"},{"id":280565,"name":"Traffic Signal Control","url":"https://www.academia.edu/Documents/in/Traffic_Signal_Control?f_ri=1032783"},{"id":357007,"name":"Machine Learning, Reinforcement Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning_Reinforcement_Learning?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"},{"id":2215017,"name":"Bayesian probability interpretation","url":"https://www.academia.edu/Documents/in/Bayesian_probability_interpretation?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_80630756" data-work_id="80630756" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/80630756/A_Bayesian_Model_for_Discovering_Typological_Implications">A Bayesian Model for Discovering Typological Implications</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A standard form of analysis for linguistic typology is the universal implication. These implications state facts about the range of extant languages, such as ``if objects come after verbs, then adjectives come after nouns.&#x27;&#x27;... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_80630756" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A standard form of analysis for linguistic typology is the universal implication. These implications state facts about the range of extant languages, such as ``if objects come after verbs, then adjectives come after nouns.&#x27;&#x27; Such implications are typically discovered by painstaking hand analysis over a small sample of languages. We propose a computational model for assisting at this process. Our model is able to discover both well-known implications as well as some novel implications that deserve further study. Moreover, through a careful application of hierarchical analysis, we are able to cope with the well-known sampling problem: languages are not independent.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/80630756" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="44091e7bead51e1957e724263c97a949" rel="nofollow" data-download="{"attachment_id":86948917,"asset_id":80630756,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/86948917/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="24632685" href="https://independent.academia.edu/CampbellLyle">Lyle Campbell</a><script data-card-contents-for-user="24632685" type="text/json">{"id":24632685,"first_name":"Lyle","last_name":"Campbell","domain_name":"independent","page_name":"CampbellLyle","display_name":"Lyle Campbell","profile_url":"https://independent.academia.edu/CampbellLyle?f_ri=1032783","photo":"https://0.academia-photos.com/24632685/6660875/7526551/s65_lyle.campbell.jpg_oh_955d414b789b23202fbe6f68125cf235_oe_5569d16f___gda___1430052606_522219e4a38793fabbe5d21edee332c1"}</script></span></span></li><li class="js-paper-rank-work_80630756 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="80630756"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 80630756, container: ".js-paper-rank-work_80630756", }); 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$(".js-view-count[data-work-id=80630756]").text(description); $(".js-view-count-work_80630756").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_80630756").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="80630756"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="85294" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Model">Computer Model</a>, <script data-card-contents-for-ri="85294" type="text/json">{"id":85294,"name":"Computer Model","url":"https://www.academia.edu/Documents/in/Computer_Model?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="154527" rel="nofollow" href="https://www.academia.edu/Documents/in/Noun">Noun</a>, <script data-card-contents-for-ri="154527" type="text/json">{"id":154527,"name":"Noun","url":"https://www.academia.edu/Documents/in/Noun?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="373540" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_model">Bayesian model</a><script data-card-contents-for-ri="373540" type="text/json">{"id":373540,"name":"Bayesian model","url":"https://www.academia.edu/Documents/in/Bayesian_model?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=80630756]'), work: {"id":80630756,"title":"A Bayesian Model for Discovering Typological Implications","created_at":"2022-06-03T11:32:37.286-07:00","url":"https://www.academia.edu/80630756/A_Bayesian_Model_for_Discovering_Typological_Implications?f_ri=1032783","dom_id":"work_80630756","summary":"A standard form of analysis for linguistic typology is the universal implication. These implications state facts about the range of extant languages, such as ``if objects come after verbs, then adjectives come after nouns.\u0026#x27;\u0026#x27; Such implications are typically discovered by painstaking hand analysis over a small sample of languages. We propose a computational model for assisting at this process. Our model is able to discover both well-known implications as well as some novel implications that deserve further study. Moreover, through a careful application of hierarchical analysis, we are able to cope with the well-known sampling problem: languages are not independent.","downloadable_attachments":[{"id":86948917,"asset_id":80630756,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":24632685,"first_name":"Lyle","last_name":"Campbell","domain_name":"independent","page_name":"CampbellLyle","display_name":"Lyle Campbell","profile_url":"https://independent.academia.edu/CampbellLyle?f_ri=1032783","photo":"https://0.academia-photos.com/24632685/6660875/7526551/s65_lyle.campbell.jpg_oh_955d414b789b23202fbe6f68125cf235_oe_5569d16f___gda___1430052606_522219e4a38793fabbe5d21edee332c1"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true},{"id":85294,"name":"Computer Model","url":"https://www.academia.edu/Documents/in/Computer_Model?f_ri=1032783","nofollow":true},{"id":154527,"name":"Noun","url":"https://www.academia.edu/Documents/in/Noun?f_ri=1032783","nofollow":true},{"id":373540,"name":"Bayesian model","url":"https://www.academia.edu/Documents/in/Bayesian_model?f_ri=1032783","nofollow":true},{"id":805001,"name":"Small samples","url":"https://www.academia.edu/Documents/in/Small_samples?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"},{"id":3193313,"name":"arXiv","url":"https://www.academia.edu/Documents/in/arXiv?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_78694513" data-work_id="78694513" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/78694513/Assessing_Scientific_Theories_The_Bayesian_Approach">Assessing Scientific Theories: The Bayesian Approach</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Scientists use a variety of methods to assess their theories. While experimental testing remains the gold standard, several other more controversial methods have been proposed, especially in fundamental physics. Amongst these methods are... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_78694513" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Scientists use a variety of methods to assess their theories. While experimental testing remains the gold standard, several other more controversial methods have been proposed, especially in fundamental physics. Amongst these methods are the use of analogue experiments and so-called non-empirical ways of theory-assessment such as the noalternatives argument. But how can these methods themselves be assessed? Are they reliable guides to the truth, or are they of no help at all when it comes to assessing scientific theories? In this chapter, we develop a general Bayesian framework to scrutinize these new (as well as standard empirical) methods of assessing scientific theories and illustrate the proposed methodology by two detailed case studies. This allows us to explore under which conditions nontraditional ways of assessing scientific theories are successful and what can be done to improve them.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/78694513" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="8cb5758935251a4d675ad28f59a349da" rel="nofollow" data-download="{"attachment_id":85652161,"asset_id":78694513,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/85652161/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="48021" href="https://lmu-munich.academia.edu/StephanHartmann">Stephan Hartmann</a><script data-card-contents-for-user="48021" type="text/json">{"id":48021,"first_name":"Stephan","last_name":"Hartmann","domain_name":"lmu-munich","page_name":"StephanHartmann","display_name":"Stephan Hartmann","profile_url":"https://lmu-munich.academia.edu/StephanHartmann?f_ri=1032783","photo":"https://0.academia-photos.com/48021/14893/11856700/s65_stephan.hartmann.jpg"}</script></span></span></li><li class="js-paper-rank-work_78694513 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="78694513"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 78694513, container: ".js-paper-rank-work_78694513", }); 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$(".js-view-count[data-work-id=78694513]").text(description); $(".js-view-count-work_78694513").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_78694513").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="78694513"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="498" rel="nofollow" href="https://www.academia.edu/Documents/in/Physics">Physics</a>, <script data-card-contents-for-ri="498" type="text/json">{"id":498,"name":"Physics","url":"https://www.academia.edu/Documents/in/Physics?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="15092" rel="nofollow" href="https://www.academia.edu/Documents/in/Evidence">Evidence</a>, <script data-card-contents-for-ri="15092" type="text/json">{"id":15092,"name":"Evidence","url":"https://www.academia.edu/Documents/in/Evidence?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="100094" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_statistics">Bayesian statistics</a>, <script data-card-contents-for-ri="100094" type="text/json">{"id":100094,"name":"Bayesian statistics","url":"https://www.academia.edu/Documents/in/Bayesian_statistics?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1032783" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Probability">Bayesian Probability</a><script data-card-contents-for-ri="1032783" type="text/json">{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=78694513]'), work: {"id":78694513,"title":"Assessing Scientific Theories: The Bayesian Approach","created_at":"2022-05-07T11:28:56.639-07:00","url":"https://www.academia.edu/78694513/Assessing_Scientific_Theories_The_Bayesian_Approach?f_ri=1032783","dom_id":"work_78694513","summary":"Scientists use a variety of methods to assess their theories. While experimental testing remains the gold standard, several other more controversial methods have been proposed, especially in fundamental physics. Amongst these methods are the use of analogue experiments and so-called non-empirical ways of theory-assessment such as the noalternatives argument. But how can these methods themselves be assessed? Are they reliable guides to the truth, or are they of no help at all when it comes to assessing scientific theories? In this chapter, we develop a general Bayesian framework to scrutinize these new (as well as standard empirical) methods of assessing scientific theories and illustrate the proposed methodology by two detailed case studies. This allows us to explore under which conditions nontraditional ways of assessing scientific theories are successful and what can be done to improve them.","downloadable_attachments":[{"id":85652161,"asset_id":78694513,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":48021,"first_name":"Stephan","last_name":"Hartmann","domain_name":"lmu-munich","page_name":"StephanHartmann","display_name":"Stephan Hartmann","profile_url":"https://lmu-munich.academia.edu/StephanHartmann?f_ri=1032783","photo":"https://0.academia-photos.com/48021/14893/11856700/s65_stephan.hartmann.jpg"}],"research_interests":[{"id":498,"name":"Physics","url":"https://www.academia.edu/Documents/in/Physics?f_ri=1032783","nofollow":true},{"id":15092,"name":"Evidence","url":"https://www.academia.edu/Documents/in/Evidence?f_ri=1032783","nofollow":true},{"id":100094,"name":"Bayesian statistics","url":"https://www.academia.edu/Documents/in/Bayesian_statistics?f_ri=1032783","nofollow":true},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_45390007" data-work_id="45390007" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/45390007/A_Normative_Theory_of_Argument_Strength">A Normative Theory of Argument Strength</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this article, we argue for the general importance of normative theories of argument strength. We also provide some evidence based on our recent work on the fallacies as to why Bayesian probability might, in fact, be able to supply such... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_45390007" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this article, we argue for the general importance of normative theories of argument strength. We also provide some evidence based on our recent work on the fallacies as to why Bayesian probability might, in fact, be able to supply such an account. In the remainder of the article we discuss the general characteristics that make a specifically Bayesian approach desirable, and critically evaluate putative flaws of Bayesian probability that have been raised in the argumentation literature.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/45390007" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="1765b56c56f81f062ada09d3ed15c407" rel="nofollow" data-download="{"attachment_id":65913210,"asset_id":45390007,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/65913210/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="37774028" href="https://independent.academia.edu/MikeOaksford">Mike Oaksford</a><script data-card-contents-for-user="37774028" type="text/json">{"id":37774028,"first_name":"Mike","last_name":"Oaksford","domain_name":"independent","page_name":"MikeOaksford","display_name":"Mike Oaksford","profile_url":"https://independent.academia.edu/MikeOaksford?f_ri=1032783","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_45390007 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="45390007"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 45390007, container: ".js-paper-rank-work_45390007", }); 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$(".js-view-count[data-work-id=45390007]").text(description); $(".js-view-count-work_45390007").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_45390007").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="45390007"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="803" rel="nofollow" href="https://www.academia.edu/Documents/in/Philosophy">Philosophy</a>, <script data-card-contents-for-ri="803" type="text/json">{"id":803,"name":"Philosophy","url":"https://www.academia.edu/Documents/in/Philosophy?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="8612" rel="nofollow" href="https://www.academia.edu/Documents/in/Argumentation">Argumentation</a>, <script data-card-contents-for-ri="8612" type="text/json">{"id":8612,"name":"Argumentation","url":"https://www.academia.edu/Documents/in/Argumentation?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="33324" rel="nofollow" href="https://www.academia.edu/Documents/in/Informal_Logic">Informal Logic</a>, <script data-card-contents-for-ri="33324" type="text/json">{"id":33324,"name":"Informal Logic","url":"https://www.academia.edu/Documents/in/Informal_Logic?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1032783" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Probability">Bayesian Probability</a><script data-card-contents-for-ri="1032783" type="text/json">{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=45390007]'), work: {"id":45390007,"title":"A Normative Theory of Argument Strength","created_at":"2021-03-06T02:53:15.658-08:00","url":"https://www.academia.edu/45390007/A_Normative_Theory_of_Argument_Strength?f_ri=1032783","dom_id":"work_45390007","summary":"In this article, we argue for the general importance of normative theories of argument strength. We also provide some evidence based on our recent work on the fallacies as to why Bayesian probability might, in fact, be able to supply such an account. In the remainder of the article we discuss the general characteristics that make a specifically Bayesian approach desirable, and critically evaluate putative flaws of Bayesian probability that have been raised in the argumentation literature.","downloadable_attachments":[{"id":65913210,"asset_id":45390007,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":37774028,"first_name":"Mike","last_name":"Oaksford","domain_name":"independent","page_name":"MikeOaksford","display_name":"Mike Oaksford","profile_url":"https://independent.academia.edu/MikeOaksford?f_ri=1032783","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":803,"name":"Philosophy","url":"https://www.academia.edu/Documents/in/Philosophy?f_ri=1032783","nofollow":true},{"id":8612,"name":"Argumentation","url":"https://www.academia.edu/Documents/in/Argumentation?f_ri=1032783","nofollow":true},{"id":33324,"name":"Informal Logic","url":"https://www.academia.edu/Documents/in/Informal_Logic?f_ri=1032783","nofollow":true},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_45389891" data-work_id="45389891" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/45389891/The_rationality_of_informal_argumentation_A_Bayesian_approach_to_reasoning_fallacies">The rationality of informal argumentation: A Bayesian approach to reasoning fallacies</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Classical informal reasoning "fallacies," for example, begging the question or arguing from ignorance, while ubiquitous in everyday argumentation, have been subject to little systematic investigation in cognitive psychology. In this... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_45389891" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Classical informal reasoning "fallacies," for example, begging the question or arguing from ignorance, while ubiquitous in everyday argumentation, have been subject to little systematic investigation in cognitive psychology. In this article it is argued that these "fallacies" provide a rich taxonomy of argument forms that can be differentially strong, dependent on their content. A Bayesian theory of content-dependent argument strength is presented. Possible psychological mechanisms are identified. Experiments are presented investigating whether people's judgments of the strength of 3 fallacies-the argumentum ad ignorantiam, the circular argument or petitio principii, and the slippery slope argument-are affected by the factors a Bayesian account predicts. This research suggests that Bayesian accounts of reasoning can be extended to the more general human activity of argumentation.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/45389891" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="891b6811462438fa600791a23895c777" rel="nofollow" data-download="{"attachment_id":65913211,"asset_id":45389891,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/65913211/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="37774028" href="https://independent.academia.edu/MikeOaksford">Mike Oaksford</a><script data-card-contents-for-user="37774028" type="text/json">{"id":37774028,"first_name":"Mike","last_name":"Oaksford","domain_name":"independent","page_name":"MikeOaksford","display_name":"Mike Oaksford","profile_url":"https://independent.academia.edu/MikeOaksford?f_ri=1032783","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_45389891 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="45389891"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 45389891, container: ".js-paper-rank-work_45389891", }); 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$(".js-view-count[data-work-id=45389891]").text(description); $(".js-view-count-work_45389891").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_45389891").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="45389891"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">19</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="236" rel="nofollow" href="https://www.academia.edu/Documents/in/Cognitive_Psychology">Cognitive Psychology</a>, <script data-card-contents-for-ri="236" type="text/json">{"id":236,"name":"Cognitive Psychology","url":"https://www.academia.edu/Documents/in/Cognitive_Psychology?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="237" rel="nofollow" href="https://www.academia.edu/Documents/in/Cognitive_Science">Cognitive Science</a>, <script data-card-contents-for-ri="237" type="text/json">{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="924" rel="nofollow" href="https://www.academia.edu/Documents/in/Logic">Logic</a>, <script data-card-contents-for-ri="924" type="text/json">{"id":924,"name":"Logic","url":"https://www.academia.edu/Documents/in/Logic?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4212" rel="nofollow" href="https://www.academia.edu/Documents/in/Cognition">Cognition</a><script data-card-contents-for-ri="4212" type="text/json">{"id":4212,"name":"Cognition","url":"https://www.academia.edu/Documents/in/Cognition?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=45389891]'), work: {"id":45389891,"title":"The rationality of informal argumentation: A Bayesian approach to reasoning fallacies","created_at":"2021-03-06T02:52:58.803-08:00","url":"https://www.academia.edu/45389891/The_rationality_of_informal_argumentation_A_Bayesian_approach_to_reasoning_fallacies?f_ri=1032783","dom_id":"work_45389891","summary":"Classical informal reasoning \"fallacies,\" for example, begging the question or arguing from ignorance, while ubiquitous in everyday argumentation, have been subject to little systematic investigation in cognitive psychology. In this article it is argued that these \"fallacies\" provide a rich taxonomy of argument forms that can be differentially strong, dependent on their content. A Bayesian theory of content-dependent argument strength is presented. Possible psychological mechanisms are identified. Experiments are presented investigating whether people's judgments of the strength of 3 fallacies-the argumentum ad ignorantiam, the circular argument or petitio principii, and the slippery slope argument-are affected by the factors a Bayesian account predicts. This research suggests that Bayesian accounts of reasoning can be extended to the more general human activity of argumentation.","downloadable_attachments":[{"id":65913211,"asset_id":45389891,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":37774028,"first_name":"Mike","last_name":"Oaksford","domain_name":"independent","page_name":"MikeOaksford","display_name":"Mike Oaksford","profile_url":"https://independent.academia.edu/MikeOaksford?f_ri=1032783","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":236,"name":"Cognitive Psychology","url":"https://www.academia.edu/Documents/in/Cognitive_Psychology?f_ri=1032783","nofollow":true},{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=1032783","nofollow":true},{"id":924,"name":"Logic","url":"https://www.academia.edu/Documents/in/Logic?f_ri=1032783","nofollow":true},{"id":4212,"name":"Cognition","url":"https://www.academia.edu/Documents/in/Cognition?f_ri=1032783","nofollow":true},{"id":7125,"name":"Culture","url":"https://www.academia.edu/Documents/in/Culture?f_ri=1032783"},{"id":8612,"name":"Argumentation","url":"https://www.academia.edu/Documents/in/Argumentation?f_ri=1032783"},{"id":66843,"name":"Judgment","url":"https://www.academia.edu/Documents/in/Judgment?f_ri=1032783"},{"id":87626,"name":"Psychological","url":"https://www.academia.edu/Documents/in/Psychological?f_ri=1032783"},{"id":97190,"name":"Informal reasoning","url":"https://www.academia.edu/Documents/in/Informal_reasoning?f_ri=1032783"},{"id":100094,"name":"Bayesian statistics","url":"https://www.academia.edu/Documents/in/Bayesian_statistics?f_ri=1032783"},{"id":172809,"name":"Interpersonal Relations","url":"https://www.academia.edu/Documents/in/Interpersonal_Relations?f_ri=1032783"},{"id":247345,"name":"Experiments","url":"https://www.academia.edu/Documents/in/Experiments?f_ri=1032783"},{"id":417542,"name":"PERSUASIVE COMMUNICATION","url":"https://www.academia.edu/Documents/in/PERSUASIVE_COMMUNICATION?f_ri=1032783"},{"id":509556,"name":"Human Activity","url":"https://www.academia.edu/Documents/in/Human_Activity?f_ri=1032783"},{"id":880279,"name":"Bayes Theorem","url":"https://www.academia.edu/Documents/in/Bayes_Theorem-1?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"},{"id":1334652,"name":"Probability Learning","url":"https://www.academia.edu/Documents/in/Probability_Learning?f_ri=1032783"},{"id":2699021,"name":"Bayesian theory","url":"https://www.academia.edu/Documents/in/Bayesian_theory?f_ri=1032783"},{"id":2752334,"name":"Bayesian approach","url":"https://www.academia.edu/Documents/in/Bayesian_approach?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_74824323" data-work_id="74824323" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/74824323/Estimates_of_the_population_pharmacokinetic_parameters_and_performance_of_Bayesian_feedback_A_sensitivity_analysis">Estimates of the population pharmacokinetic parameters and performance of Bayesian feedback: A sensitivity analysis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We investigated the influence of bias in the estimates of the population pharmacokinetic parameters on the performance of Bayesian feedback in achieving a desired drug serum concentration. Three specific cases were considered (i)... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_74824323" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We investigated the influence of bias in the estimates of the population pharmacokinetic parameters on the performance of Bayesian feedback in achieving a desired drug serum concentration. Three specific cases were considered (i) steady-state case, (ii) lidocaine example, and (iii) mexiletine example. Whereas in the first case both the feedback and the desired concentration represented steady-state values, in the lidocaine and mexiletine examples the feedback concentration was assumed to be sampled shortly after starting therapy. RMSE was used as a measure of predictive performance. For the simple steady-state case the relationship between RMSE and bias in the parameter estimates describing the prior distribution could be derived analytically. Monte Carlo simulations were used to explore the two non-steady-state situations. In general, the performance of Bayesian feedback to predict serum concentrations was relatively insensitive to bad population parameter estimates. However, large changes in RMSE could be observed with small changes in the true variance component parameters in particular in the intraindividual residual variance, sigma 2 epsilon, indicating that the prediction interval, in contrast to point prediction, is sensitive to bias in the estimates of the population parameters.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/74824323" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="161724981" href="https://independent.academia.edu/ChristophSteiner3">Christoph Steiner</a><script data-card-contents-for-user="161724981" type="text/json">{"id":161724981,"first_name":"Christoph","last_name":"Steiner","domain_name":"independent","page_name":"ChristophSteiner3","display_name":"Christoph Steiner","profile_url":"https://independent.academia.edu/ChristophSteiner3?f_ri=1032783","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_74824323 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="74824323"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 74824323, container: ".js-paper-rank-work_74824323", }); 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Three specific cases were considered (i) steady-state case, (ii) lidocaine example, and (iii) mexiletine example. Whereas in the first case both the feedback and the desired concentration represented steady-state values, in the lidocaine and mexiletine examples the feedback concentration was assumed to be sampled shortly after starting therapy. RMSE was used as a measure of predictive performance. For the simple steady-state case the relationship between RMSE and bias in the parameter estimates describing the prior distribution could be derived analytically. Monte Carlo simulations were used to explore the two non-steady-state situations. In general, the performance of Bayesian feedback to predict serum concentrations was relatively insensitive to bad population parameter estimates. However, large changes in RMSE could be observed with small changes in the true variance component parameters in particular in the intraindividual residual variance, sigma 2 epsilon, indicating that the prediction interval, in contrast to point prediction, is sensitive to bias in the estimates of the population parameters.","downloadable_attachments":[],"ordered_authors":[{"id":161724981,"first_name":"Christoph","last_name":"Steiner","domain_name":"independent","page_name":"ChristophSteiner3","display_name":"Christoph Steiner","profile_url":"https://independent.academia.edu/ChristophSteiner3?f_ri=1032783","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=1032783","nofollow":true},{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1032783","nofollow":true},{"id":4392,"name":"Monte Carlo 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class="InlineList-item-text" data-has-card-for-ri="2534" rel="nofollow" href="https://www.academia.edu/Documents/in/Multiagent_Systems">Multiagent Systems</a>, <script data-card-contents-for-ri="2534" type="text/json">{"id":2534,"name":"Multiagent Systems","url":"https://www.academia.edu/Documents/in/Multiagent_Systems?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="3252" rel="nofollow" href="https://www.academia.edu/Documents/in/Traffic_Simulation">Traffic Simulation</a><script data-card-contents-for-ri="3252" type="text/json">{"id":3252,"name":"Traffic Simulation","url":"https://www.academia.edu/Documents/in/Traffic_Simulation?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=11055457]'), work: {"id":11055457,"title":"Adaptive traffic control system based on Bayesian probability 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class="summarized">A new method has been developed for assessment of the onset of degradation in solid state luminaires to classify failure mechanisms by using metrics beyond lumen degradation that are currently used for identification of failure. Luminous... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_23667685" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A new method has been developed for assessment of the onset of degradation in solid state luminaires to classify failure mechanisms by using metrics beyond lumen degradation that are currently used for identification of failure. Luminous Flux output, Correlated Color Temperature Data on Philips LED Lamps has been gathered under 85°C/85%RH till lamp failure. Failure modes of the test population of the lamps have been studied to understand the failure mechanisms in 85°C/85%RH accelerated test. Results indicate that the dominant failure mechanism is the discoloration of the LED encapsulant inside the lamps which is the likely cause for the luminous flux degradation and the color shift. The acquired data has been used in conjunction with Bayesian Probabilistic Models to identify luminaires with onset of degradation much prior to failure through identification of decision boundaries between lamps with accrued damage and lamps beyond the failure threshold in the feature space. In addition luminaires with different failure modes have been classified separately from healthy pristine luminaires. The -plots have been used to evaluate the robustness of the proposed methodology. Results show that the predicted degradation for the lamps tracks the true degradation observed during 85°C/85%RH during accelerated life test fairly closely within the ±20% confidence bounds. Correlation of model prediction with experimental results indicates that the presented methodology allows the early identification of the onset of failure much prior to development of complete failure distributions and can be used for assessing the damage state of SSLs in fairly large deployments. It is expected that, the new prediction technique will allow the development of failure distributions without testing till L70 life for the manifestation of failure.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/23667685" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f77d5524168c4a947187298f114d49b9" rel="nofollow" data-download="{"attachment_id":44072786,"asset_id":23667685,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/44072786/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="45669035" href="https://independent.academia.edu/PradeepLall">Pradeep Lall</a><script data-card-contents-for-user="45669035" type="text/json">{"id":45669035,"first_name":"Pradeep","last_name":"Lall","domain_name":"independent","page_name":"PradeepLall","display_name":"Pradeep Lall","profile_url":"https://independent.academia.edu/PradeepLall?f_ri=1032783","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_23667685 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="23667685"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 23667685, container: ".js-paper-rank-work_23667685", }); 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Luminous Flux output, Correlated Color Temperature Data on Philips LED Lamps has been gathered under 85°C/85%RH till lamp failure. Failure modes of the test population of the lamps have been studied to understand the failure mechanisms in 85°C/85%RH accelerated test. Results indicate that the dominant failure mechanism is the discoloration of the LED encapsulant inside the lamps which is the likely cause for the luminous flux degradation and the color shift. The acquired data has been used in conjunction with Bayesian Probabilistic Models to identify luminaires with onset of degradation much prior to failure through identification of decision boundaries between lamps with accrued damage and lamps beyond the failure threshold in the feature space. In addition luminaires with different failure modes have been classified separately from healthy pristine luminaires. The -plots have been used to evaluate the robustness of the proposed methodology. Results show that the predicted degradation for the lamps tracks the true degradation observed during 85°C/85%RH during accelerated life test fairly closely within the ±20% confidence bounds. Correlation of model prediction with experimental results indicates that the presented methodology allows the early identification of the onset of failure much prior to development of complete failure distributions and can be used for assessing the damage state of SSLs in fairly large deployments. It is expected that, the new prediction technique will allow the development of failure distributions without testing till L70 life for the manifestation of failure.","downloadable_attachments":[{"id":44072786,"asset_id":23667685,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":45669035,"first_name":"Pradeep","last_name":"Lall","domain_name":"independent","page_name":"PradeepLall","display_name":"Pradeep Lall","profile_url":"https://independent.academia.edu/PradeepLall?f_ri=1032783","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":45676,"name":"Failure Analysis","url":"https://www.academia.edu/Documents/in/Failure_Analysis?f_ri=1032783","nofollow":true},{"id":163878,"name":"Degradation","url":"https://www.academia.edu/Documents/in/Degradation?f_ri=1032783","nofollow":true},{"id":165126,"name":"Maintenance Engineering","url":"https://www.academia.edu/Documents/in/Maintenance_Engineering?f_ri=1032783","nofollow":true},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true},{"id":1271926,"name":"Solids","url":"https://www.academia.edu/Documents/in/Solids?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_18510664 coauthored" data-work_id="18510664" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/18510664/Multi_Objective_Traffic_Light_Control_System_based_on_Bayesian_Probability_Interpretation">Multi-Objective Traffic Light Control System based on Bayesian Probability Interpretation</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Traffic light control is a challenging problem in modern societies. This is due to the huge number of vehicles and the high dynamics of the traffic system. Poor traffic management causes a high rate of accidents, time losses, and... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_18510664" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Traffic light control is a challenging problem in modern societies. This is due to the huge number of vehicles and the high dynamics of the traffic system. Poor traffic management causes a high rate of accidents, time losses, and negative impact on the economy as well as the environment. In this paper, we develop a multiagent traffic light control system based on a multi-objective sequential decision making framework. In order to respond effectively to the changing environment and the non-stationarity of the road network, our traffic light controller is based on the Bayesian interpretation of probability. We use the open source Green Light District (GLD) vehicle traffic simulator as a testbed framework. The change in road conditions is modeled by varying the vehicles generation probability distributions and adapting the Intelligent Driver Model (IDM) parameters to the adverse weather conditions. We have added a set of new performance indices in GLD based on collaborative learning to better evaluate the performance of our multi-objective traffic controller.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/18510664" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="35f5278bbb7a6bc67db3573c421c563f" rel="nofollow" data-download="{"attachment_id":40103089,"asset_id":18510664,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/40103089/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="37877592" href="https://ejust.academia.edu/walid_gomaa">Walid Gomaa</a><script data-card-contents-for-user="37877592" type="text/json">{"id":37877592,"first_name":"Walid","last_name":"Gomaa","domain_name":"ejust","page_name":"walid_gomaa","display_name":"Walid Gomaa","profile_url":"https://ejust.academia.edu/walid_gomaa?f_ri=1032783","photo":"https://0.academia-photos.com/37877592/10632393/11942071/s65_walid.gomaa.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-18510664">+1</span><div class="hidden js-additional-users-18510664"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/HishamElshishiny">Hisham El-shishiny</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-18510664'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-18510664').html(); 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This is due to the huge number of vehicles and the high dynamics of the traffic system. Poor traffic management causes a high rate of accidents, time losses, and negative impact on the economy as well as the environment. In this paper, we develop a multiagent traffic light control system based on a multi-objective sequential decision making framework. In order to respond effectively to the changing environment and the non-stationarity of the road network, our traffic light controller is based on the Bayesian interpretation of probability. We use the open source Green Light District (GLD) vehicle traffic simulator as a testbed framework. The change in road conditions is modeled by varying the vehicles generation probability distributions and adapting the Intelligent Driver Model (IDM) parameters to the adverse weather conditions. We have added a set of new performance indices in GLD based on collaborative learning to better evaluate the performance of our multi-objective traffic controller.","downloadable_attachments":[{"id":40103089,"asset_id":18510664,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":37877592,"first_name":"Walid","last_name":"Gomaa","domain_name":"ejust","page_name":"walid_gomaa","display_name":"Walid Gomaa","profile_url":"https://ejust.academia.edu/walid_gomaa?f_ri=1032783","photo":"https://0.academia-photos.com/37877592/10632393/11942071/s65_walid.gomaa.png"},{"id":34927416,"first_name":"Hisham","last_name":"El-shishiny","domain_name":"independent","page_name":"HishamElshishiny","display_name":"Hisham El-shishiny","profile_url":"https://independent.academia.edu/HishamElshishiny?f_ri=1032783","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":13445,"name":"Multiobjective Optimization","url":"https://www.academia.edu/Documents/in/Multiobjective_Optimization?f_ri=1032783","nofollow":true},{"id":22247,"name":"Traffic control","url":"https://www.academia.edu/Documents/in/Traffic_control?f_ri=1032783","nofollow":true},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783","nofollow":true},{"id":103411,"name":"Traffic management and control","url":"https://www.academia.edu/Documents/in/Traffic_management_and_control?f_ri=1032783","nofollow":true},{"id":171813,"name":"Multiobjective","url":"https://www.academia.edu/Documents/in/Multiobjective?f_ri=1032783"},{"id":280565,"name":"Traffic Signal Control","url":"https://www.academia.edu/Documents/in/Traffic_Signal_Control?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"},{"id":2215017,"name":"Bayesian probability interpretation","url":"https://www.academia.edu/Documents/in/Bayesian_probability_interpretation?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_81852765" data-work_id="81852765" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/81852765/Robustness_of_Compound_Dirichlet_Priors_for_Bayesian_Inference_of_Branch_Lengths">Robustness of Compound Dirichlet Priors for Bayesian Inference of Branch Lengths</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We modified the phylogenetic program MrBayes 3.1.2 to incorporate the compound Dirichlet priors for branch lengths proposed recently by Rannala, Zhu, and Yang (2012. Tail paradox, partial identifiability and influential priors in Bayesian... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_81852765" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We modified the phylogenetic program MrBayes 3.1.2 to incorporate the compound Dirichlet priors for branch lengths proposed recently by Rannala, Zhu, and Yang (2012. Tail paradox, partial identifiability and influential priors in Bayesian branch length inference. as a solution to the problem of branch-length overestimation in Bayesian phylogenetic inference. The compound Dirichlet prior specifies a fairly diffuse prior on the tree length (the sum of branch lengths) and uses a Dirichlet distribution to partition the tree length into branch lengths. Six problematic data sets originally analyzed by Brown, Hedtke, Lemmon, and Lemmon (2010. When trees grow too long: investigating the causes of highly inaccurate Bayesian branch-length estimates. Syst. Biol. 59:145-161) are reanalyzed using the modified version of MrBayes to investigate properties of Bayesian branch-length estimation using the new priors. While the default exponential priors for branch lengths produced extremely long trees, the compound Dirichlet priors produced posterior estimates that are much closer to the maximum likelihood estimates. Furthermore, the posterior tree lengths were quite robust to changes in the parameter values in the compound Dirichlet priors, for example, when the prior mean of tree length changed over several orders of magnitude. Our results suggest that the compound Dirichlet priors may be useful for correcting branchlength overestimation in phylogenetic analyses of empirical data sets. [Bayesian phylogenetics; branch lengths; compound Dirichlet prior; MrBayes.]</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/81852765" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="dd362ec6ae50473ec95205839d8b37c0" rel="nofollow" data-download="{"attachment_id":87752247,"asset_id":81852765,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/87752247/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="56368731" href="https://independent.academia.edu/ZihengYang1">Ziheng Yang</a><script data-card-contents-for-user="56368731" type="text/json">{"id":56368731,"first_name":"Ziheng","last_name":"Yang","domain_name":"independent","page_name":"ZihengYang1","display_name":"Ziheng Yang","profile_url":"https://independent.academia.edu/ZihengYang1?f_ri=1032783","photo":"https://0.academia-photos.com/56368731/15790264/16315810/s65_ziheng.yang.jpg"}</script></span></span></li><li class="js-paper-rank-work_81852765 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="81852765"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 81852765, container: ".js-paper-rank-work_81852765", }); 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Tail paradox, partial identifiability and influential priors in Bayesian branch length inference. as a solution to the problem of branch-length overestimation in Bayesian phylogenetic inference. The compound Dirichlet prior specifies a fairly diffuse prior on the tree length (the sum of branch lengths) and uses a Dirichlet distribution to partition the tree length into branch lengths. Six problematic data sets originally analyzed by Brown, Hedtke, Lemmon, and Lemmon (2010. When trees grow too long: investigating the causes of highly inaccurate Bayesian branch-length estimates. Syst. Biol. 59:145-161) are reanalyzed using the modified version of MrBayes to investigate properties of Bayesian branch-length estimation using the new priors. While the default exponential priors for branch lengths produced extremely long trees, the compound Dirichlet priors produced posterior estimates that are much closer to the maximum likelihood estimates. Furthermore, the posterior tree lengths were quite robust to changes in the parameter values in the compound Dirichlet priors, for example, when the prior mean of tree length changed over several orders of magnitude. Our results suggest that the compound Dirichlet priors may be useful for correcting branchlength overestimation in phylogenetic analyses of empirical data sets. [Bayesian phylogenetics; branch lengths; compound Dirichlet prior; MrBayes.]","downloadable_attachments":[{"id":87752247,"asset_id":81852765,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":56368731,"first_name":"Ziheng","last_name":"Yang","domain_name":"independent","page_name":"ZihengYang1","display_name":"Ziheng Yang","profile_url":"https://independent.academia.edu/ZihengYang1?f_ri=1032783","photo":"https://0.academia-photos.com/56368731/15790264/16315810/s65_ziheng.yang.jpg"}],"research_interests":[{"id":155,"name":"Evolutionary Biology","url":"https://www.academia.edu/Documents/in/Evolutionary_Biology?f_ri=1032783","nofollow":true},{"id":156,"name":"Genetics","url":"https://www.academia.edu/Documents/in/Genetics?f_ri=1032783","nofollow":true},{"id":3363,"name":"Systematic Biology","url":"https://www.academia.edu/Documents/in/Systematic_Biology?f_ri=1032783","nofollow":true},{"id":18574,"name":"Inference","url":"https://www.academia.edu/Documents/in/Inference?f_ri=1032783","nofollow":true},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783"},{"id":54433,"name":"Phylogeny","url":"https://www.academia.edu/Documents/in/Phylogeny?f_ri=1032783"},{"id":65140,"name":"Models","url":"https://www.academia.edu/Documents/in/Models?f_ri=1032783"},{"id":69542,"name":"Computer Simulation","url":"https://www.academia.edu/Documents/in/Computer_Simulation?f_ri=1032783"},{"id":85920,"name":"Bivalvia","url":"https://www.academia.edu/Documents/in/Bivalvia?f_ri=1032783"},{"id":92463,"name":"Lizards","url":"https://www.academia.edu/Documents/in/Lizards?f_ri=1032783"},{"id":106145,"name":"Classification","url":"https://www.academia.edu/Documents/in/Classification?f_ri=1032783"},{"id":133857,"name":"Anura","url":"https://www.academia.edu/Documents/in/Anura?f_ri=1032783"},{"id":880279,"name":"Bayes Theorem","url":"https://www.academia.edu/Documents/in/Bayes_Theorem-1?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_81478715" data-work_id="81478715" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/81478715/Bayesian_optimum_designs_for_discriminating_between_models_with_any_distribution">Bayesian optimum designs for discriminating between models with any distribution</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The Bayesian KL-optimality criterion is useful for discriminating between any two statistical models in the presence of prior information. If the rival models are not nested then, depending on which model is true, two different... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_81478715" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The Bayesian KL-optimality criterion is useful for discriminating between any two statistical models in the presence of prior information. If the rival models are not nested then, depending on which model is true, two different Kullback–Leibler distances may be defined. The Bayesian KL-optimality criterion is a convex combination of the expected values of these two possible Kullback–Leibler distances between the</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/81478715" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="8708896" href="https://independent.academia.edu/Jes%C3%BAsL%C3%B3pezFidalgo">Jesús López Fidalgo</a><script data-card-contents-for-user="8708896" type="text/json">{"id":8708896,"first_name":"Jesús","last_name":"López Fidalgo","domain_name":"independent","page_name":"JesúsLópezFidalgo","display_name":"Jesús López Fidalgo","profile_url":"https://independent.academia.edu/Jes%C3%BAsL%C3%B3pezFidalgo?f_ri=1032783","photo":"https://0.academia-photos.com/8708896/86991191/75664757/s65_jes_s.l_pez_fidalgo.jpeg"}</script></span></span></li><li class="js-paper-rank-work_81478715 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="81478715"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 81478715, container: ".js-paper-rank-work_81478715", }); 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If the rival models are not nested then, depending on which model is true, two different Kullback–Leibler distances may be defined. The Bayesian KL-optimality criterion is a convex combination of the expected values of these two possible Kullback–Leibler distances between the","downloadable_attachments":[],"ordered_authors":[{"id":8708896,"first_name":"Jesús","last_name":"López Fidalgo","domain_name":"independent","page_name":"JesúsLópezFidalgo","display_name":"Jesús López Fidalgo","profile_url":"https://independent.academia.edu/Jes%C3%BAsL%C3%B3pezFidalgo?f_ri=1032783","photo":"https://0.academia-photos.com/8708896/86991191/75664757/s65_jes_s.l_pez_fidalgo.jpeg"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=1032783","nofollow":true},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true},{"id":747,"name":"Econometrics","url":"https://www.academia.edu/Documents/in/Econometrics?f_ri=1032783","nofollow":true},{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1032783","nofollow":true},{"id":4205,"name":"Data Analysis","url":"https://www.academia.edu/Documents/in/Data_Analysis?f_ri=1032783"},{"id":21747,"name":"Experimental Design","url":"https://www.academia.edu/Documents/in/Experimental_Design?f_ri=1032783"},{"id":67959,"name":"Probability Distribution \u0026 Applications","url":"https://www.academia.edu/Documents/in/Probability_Distribution_and_Applications?f_ri=1032783"},{"id":86986,"name":"Kullback-Leibler distance","url":"https://www.academia.edu/Documents/in/Kullback-Leibler_distance?f_ri=1032783"},{"id":594207,"name":"Expectation","url":"https://www.academia.edu/Documents/in/Expectation?f_ri=1032783"},{"id":664700,"name":"Statistical Model","url":"https://www.academia.edu/Documents/in/Statistical_Model?f_ri=1032783"},{"id":789521,"name":"Optimal Design","url":"https://www.academia.edu/Documents/in/Optimal_Design?f_ri=1032783"},{"id":850827,"name":"Optimum Design","url":"https://www.academia.edu/Documents/in/Optimum_Design?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"},{"id":1496485,"name":"Computational Statistics and Data Analysis","url":"https://www.academia.edu/Documents/in/Computational_Statistics_and_Data_Analysis?f_ri=1032783"},{"id":1808788,"name":"Convex Combination","url":"https://www.academia.edu/Documents/in/Convex_Combination?f_ri=1032783"},{"id":2814568,"name":"Probability Distribution","url":"https://www.academia.edu/Documents/in/Probability_Distribution?f_ri=1032783"},{"id":2891350,"name":"Distribution Function","url":"https://www.academia.edu/Documents/in/Distribution_Function-1?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_79475002" data-work_id="79475002" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/79475002/QuLBIT_Quantum_Like_Bayesian_Inference_Technologies_for_Cognition_and_Decision">QuLBIT: Quantum-Like Bayesian Inference Technologies for Cognition and Decision</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper provides the foundations of a unified cognitive decision-making framework (QulBIT) which is derived from quantum theory. The main advantage of this framework is that it can cater for paradoxical and irrational human decision... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_79475002" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper provides the foundations of a unified cognitive decision-making framework (QulBIT) which is derived from quantum theory. The main advantage of this framework is that it can cater for paradoxical and irrational human decision making. Although quantum approaches for cognition have demonstrated advantages over classical probabilistic approaches and bounded rationality models, they still lack explanatory power. To address this, we introduce a novel explanatory analysis of the decision-maker&#39;s belief space. This is achieved by exploiting quantum interference effects as a way of both quantifying and explaining the decision-maker&#39;s uncertainty. We detail the main modules of the unified framework, the explanatory analysis method, and illustrate their application in situations violating the Sure Thing Principle.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/79475002" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="06e0f306ce08aaa313b9147aa93a5d2b" rel="nofollow" data-download="{"attachment_id":86177310,"asset_id":79475002,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/86177310/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="406616" href="https://qut.academia.edu/PeterBruza">Peter Bruza</a><script data-card-contents-for-user="406616" type="text/json">{"id":406616,"first_name":"Peter","last_name":"Bruza","domain_name":"qut","page_name":"PeterBruza","display_name":"Peter Bruza","profile_url":"https://qut.academia.edu/PeterBruza?f_ri=1032783","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_79475002 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="79475002"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 79475002, container: ".js-paper-rank-work_79475002", }); 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$(".js-view-count[data-work-id=79475002]").text(description); $(".js-view-count-work_79475002").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_79475002").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="79475002"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="237" rel="nofollow" href="https://www.academia.edu/Documents/in/Cognitive_Science">Cognitive Science</a>, <script data-card-contents-for-ri="237" type="text/json">{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="18574" rel="nofollow" href="https://www.academia.edu/Documents/in/Inference">Inference</a>, <script data-card-contents-for-ri="18574" type="text/json">{"id":18574,"name":"Inference","url":"https://www.academia.edu/Documents/in/Inference?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="51529" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Inference">Bayesian Inference</a><script data-card-contents-for-ri="51529" type="text/json">{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=79475002]'), work: {"id":79475002,"title":"QuLBIT: Quantum-Like Bayesian Inference Technologies for Cognition and Decision","created_at":"2022-05-19T15:27:37.514-07:00","url":"https://www.academia.edu/79475002/QuLBIT_Quantum_Like_Bayesian_Inference_Technologies_for_Cognition_and_Decision?f_ri=1032783","dom_id":"work_79475002","summary":"This paper provides the foundations of a unified cognitive decision-making framework (QulBIT) which is derived from quantum theory. The main advantage of this framework is that it can cater for paradoxical and irrational human decision making. Although quantum approaches for cognition have demonstrated advantages over classical probabilistic approaches and bounded rationality models, they still lack explanatory power. To address this, we introduce a novel explanatory analysis of the decision-maker\u0026#39;s belief space. This is achieved by exploiting quantum interference effects as a way of both quantifying and explaining the decision-maker\u0026#39;s uncertainty. We detail the main modules of the unified framework, the explanatory analysis method, and illustrate their application in situations violating the Sure Thing Principle.","downloadable_attachments":[{"id":86177310,"asset_id":79475002,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":406616,"first_name":"Peter","last_name":"Bruza","domain_name":"qut","page_name":"PeterBruza","display_name":"Peter Bruza","profile_url":"https://qut.academia.edu/PeterBruza?f_ri=1032783","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=1032783","nofollow":true},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true},{"id":18574,"name":"Inference","url":"https://www.academia.edu/Documents/in/Inference?f_ri=1032783","nofollow":true},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783","nofollow":true},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_75093518" data-work_id="75093518" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/75093518/An_Information_Systems_Teaching_Case_Bayesian_Probability_Applied_to_Spam_eMail_Filters">An Information Systems Teaching Case: Bayesian Probability Applied to Spam eMail Filters</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Information Systems professionals can participate in the strategic planning and policy development of the business organization by applying sound techniques for rational decision making. Decision Support Systems often utilize inferential... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_75093518" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Information Systems professionals can participate in the strategic planning and policy development of the business organization by applying sound techniques for rational decision making. Decision Support Systems often utilize inferential techniques to provide analysis and knowledge creation for business and its information systems. One common method of reasoning under uncertainty is the application of the Bayesian probability model. This teaching case can be used in an Information Systems program to teach one method of inferential reasoning as applied to policy and business rules for spam email filters.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/75093518" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="215395485" href="https://independent.academia.edu/SamuelConn2">Samuel Conn</a><script data-card-contents-for-user="215395485" type="text/json">{"id":215395485,"first_name":"Samuel","last_name":"Conn","domain_name":"independent","page_name":"SamuelConn2","display_name":"Samuel Conn","profile_url":"https://independent.academia.edu/SamuelConn2?f_ri=1032783","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_75093518 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="75093518"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 75093518, container: ".js-paper-rank-work_75093518", }); 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Decision Support Systems often utilize inferential techniques to provide analysis and knowledge creation for business and its information systems. One common method of reasoning under uncertainty is the application of the Bayesian probability model. This teaching case can be used in an Information Systems program to teach one method of inferential reasoning as applied to policy and business rules for spam email filters.","downloadable_attachments":[],"ordered_authors":[{"id":215395485,"first_name":"Samuel","last_name":"Conn","domain_name":"independent","page_name":"SamuelConn2","display_name":"Samuel Conn","profile_url":"https://independent.academia.edu/SamuelConn2?f_ri=1032783","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true},{"id":1681,"name":"Decision Making","url":"https://www.academia.edu/Documents/in/Decision_Making?f_ri=1032783","nofollow":true},{"id":7739,"name":"Strategic Planning","url":"https://www.academia.edu/Documents/in/Strategic_Planning?f_ri=1032783","nofollow":true},{"id":10722,"name":"Policy Development","url":"https://www.academia.edu/Documents/in/Policy_Development?f_ri=1032783","nofollow":true},{"id":45874,"name":"Decision support system","url":"https://www.academia.edu/Documents/in/Decision_support_system?f_ri=1032783"},{"id":49560,"name":"Knowledge Creation","url":"https://www.academia.edu/Documents/in/Knowledge_Creation?f_ri=1032783"},{"id":170984,"name":"Teaching Case","url":"https://www.academia.edu/Documents/in/Teaching_Case?f_ri=1032783"},{"id":202582,"name":"Business rules","url":"https://www.academia.edu/Documents/in/Business_rules?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"},{"id":1820028,"name":"Probability Model","url":"https://www.academia.edu/Documents/in/Probability_Model?f_ri=1032783"},{"id":2213585,"name":"Information System","url":"https://www.academia.edu/Documents/in/Information_System?f_ri=1032783"},{"id":2330105,"name":"REASONING UNDER UNCERTAINTY","url":"https://www.academia.edu/Documents/in/REASONING_UNDER_UNCERTAINTY?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_74810608" data-work_id="74810608" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/74810608/Bayesian_Inference_Based_on_Stationary_Fokker_Planck_Sampling">Bayesian Inference Based on Stationary Fokker-Planck Sampling</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A novel formalism for Bayesian learning in the context of complex inference models is proposed. The method is based on the use of the Stationary Fokker--Planck (SFP) approach to sample from the posterior density. Stationary Fokker--Planck... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_74810608" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A novel formalism for Bayesian learning in the context of complex inference models is proposed. The method is based on the use of the Stationary Fokker--Planck (SFP) approach to sample from the posterior density. Stationary Fokker--Planck sampling generalizes the Gibbs sampler algorithm for arbitrary and unknown conditional densities. By the SFP procedure approximate analytical expressions for the conditionals and marginals of the posterior can be constructed. At each stage of SFP, the approximate conditionals are used to define a Gibbs sampling process, which is convergent to the full joint posterior. By the analytical marginals efficient learning methods in the context of Artificial Neural Networks are outlined. Off--line and incremental Bayesian inference and Maximum Likelihood Estimation from the posterior is performed in classification and regression examples. A comparison of SFP with other Monte Carlo strategies in the general problem of sampling from arbitrary densities is al...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/74810608" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="69442b19989fb543ba328392d637a9ce" rel="nofollow" data-download="{"attachment_id":82827496,"asset_id":74810608,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/82827496/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="712408" href="https://independent.academia.edu/ArturoBerrones">Arturo Berrones-Santos</a><script data-card-contents-for-user="712408" type="text/json">{"id":712408,"first_name":"Arturo","last_name":"Berrones-Santos","domain_name":"independent","page_name":"ArturoBerrones","display_name":"Arturo Berrones-Santos","profile_url":"https://independent.academia.edu/ArturoBerrones?f_ri=1032783","photo":"https://0.academia-photos.com/712408/78798194/67354877/s65_arturo.berrones.png"}</script></span></span></li><li class="js-paper-rank-work_74810608 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="74810608"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 74810608, container: ".js-paper-rank-work_74810608", }); 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The method is based on the use of the Stationary Fokker--Planck (SFP) approach to sample from the posterior density. Stationary Fokker--Planck sampling generalizes the Gibbs sampler algorithm for arbitrary and unknown conditional densities. By the SFP procedure approximate analytical expressions for the conditionals and marginals of the posterior can be constructed. At each stage of SFP, the approximate conditionals are used to define a Gibbs sampling process, which is convergent to the full joint posterior. By the analytical marginals efficient learning methods in the context of Artificial Neural Networks are outlined. Off--line and incremental Bayesian inference and Maximum Likelihood Estimation from the posterior is performed in classification and regression examples. 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The method is based on the use of the Stationary Fokker-Planck (SFP) approach to sample from the posterior density. Stationary Fokker-Planck... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_74810595" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A novel formalism for Bayesian learning in the context of complex inference models is proposed. The method is based on the use of the Stationary Fokker-Planck (SFP) approach to sample from the posterior density. Stationary Fokker-Planck sampling generalizes the Gibbs sampler algorithm for arbitrary and unknown conditional densities. By the SFP procedure approximate analytical expressions for the conditionals and marginals of the posterior can be constructed. At each stage of SFP, the approximate conditionals are used to define a Gibbs sampling process, which is convergent to the full joint posterior. By the analytical marginals efficient learning methods in the context of Artificial Neural Networks are outlined. Off-line and incremental Bayesian inference and Maximum Likelihood Estimation from the posterior is performed in classification and regression examples. A comparison of SFP with other Monte Carlo strategies in the general problem of sampling from arbitrary densities is also presented. It is shown that SFP is able to jump large low-probabilty regions without the need of a careful tuning of any step size parameter. In fact, the SFP method requires only a small set of meaningful parameters which</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/74810595" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="70c263e3659eff24a6fa9e9ca666d574" rel="nofollow" data-download="{"attachment_id":82827540,"asset_id":74810595,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/82827540/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="712408" href="https://independent.academia.edu/ArturoBerrones">Arturo Berrones-Santos</a><script data-card-contents-for-user="712408" type="text/json">{"id":712408,"first_name":"Arturo","last_name":"Berrones-Santos","domain_name":"independent","page_name":"ArturoBerrones","display_name":"Arturo Berrones-Santos","profile_url":"https://independent.academia.edu/ArturoBerrones?f_ri=1032783","photo":"https://0.academia-photos.com/712408/78798194/67354877/s65_arturo.berrones.png"}</script></span></span></li><li class="js-paper-rank-work_74810595 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="74810595"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 74810595, container: ".js-paper-rank-work_74810595", }); 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The method is based on the use of the Stationary Fokker-Planck (SFP) approach to sample from the posterior density. Stationary Fokker-Planck sampling generalizes the Gibbs sampler algorithm for arbitrary and unknown conditional densities. By the SFP procedure approximate analytical expressions for the conditionals and marginals of the posterior can be constructed. At each stage of SFP, the approximate conditionals are used to define a Gibbs sampling process, which is convergent to the full joint posterior. By the analytical marginals efficient learning methods in the context of Artificial Neural Networks are outlined. Off-line and incremental Bayesian inference and Maximum Likelihood Estimation from the posterior is performed in classification and regression examples. A comparison of SFP with other Monte Carlo strategies in the general problem of sampling from arbitrary densities is also presented. It is shown that SFP is able to jump large low-probabilty regions without the need of a careful tuning of any step size parameter. In fact, the SFP method requires only a small set of meaningful parameters which","downloadable_attachments":[{"id":82827540,"asset_id":74810595,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":712408,"first_name":"Arturo","last_name":"Berrones-Santos","domain_name":"independent","page_name":"ArturoBerrones","display_name":"Arturo Berrones-Santos","profile_url":"https://independent.academia.edu/ArturoBerrones?f_ri=1032783","photo":"https://0.academia-photos.com/712408/78798194/67354877/s65_arturo.berrones.png"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=1032783","nofollow":true},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true},{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=1032783","nofollow":true},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=1032783","nofollow":true},{"id":4205,"name":"Data Analysis","url":"https://www.academia.edu/Documents/in/Data_Analysis?f_ri=1032783"},{"id":6974,"name":"Monte Carlo","url":"https://www.academia.edu/Documents/in/Monte_Carlo?f_ri=1032783"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine?f_ri=1032783"},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783"},{"id":55378,"name":"Bayesian Learning","url":"https://www.academia.edu/Documents/in/Bayesian_Learning?f_ri=1032783"},{"id":69542,"name":"Computer Simulation","url":"https://www.academia.edu/Documents/in/Computer_Simulation?f_ri=1032783"},{"id":229603,"name":"Gibbs sampling","url":"https://www.academia.edu/Documents/in/Gibbs_sampling?f_ri=1032783"},{"id":252813,"name":"Evolutionary 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function","url":"https://www.academia.edu/Documents/in/Loss_function?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_5360577" data-work_id="5360577" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/5360577/A_Bayesian_Analysis_of_Pascals_Wager_for_GAP_8">A Bayesian Analysis of Pascal's Wager for GAP.8</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/5360577" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span 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class="u-tcGrayDark u-fw700" data-has-card-for-user="7413601" href="https://erfurt.academia.edu/StamatiosGerogiorgakis">Stamatios Gerogiorgakis</a><script data-card-contents-for-user="7413601" type="text/json">{"id":7413601,"first_name":"Stamatios","last_name":"Gerogiorgakis","domain_name":"erfurt","page_name":"StamatiosGerogiorgakis","display_name":"Stamatios Gerogiorgakis","profile_url":"https://erfurt.academia.edu/StamatiosGerogiorgakis?f_ri=1032783","photo":"https://0.academia-photos.com/7413601/3004621/3528508/s65_stamatios.gerogiorgakis.png"}</script></span></span></li><li class="js-paper-rank-work_5360577 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="5360577"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 5360577, container: ".js-paper-rank-work_5360577", }); });</script></li><li 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description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=5360577]").text(description); $(".js-view-count-work_5360577").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_5360577").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="5360577"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="902" rel="nofollow" href="https://www.academia.edu/Documents/in/Philosophy_Of_Religion">Philosophy Of Religion</a>, <script data-card-contents-for-ri="902" type="text/json">{"id":902,"name":"Philosophy Of Religion","url":"https://www.academia.edu/Documents/in/Philosophy_Of_Religion?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="374882" rel="nofollow" href="https://www.academia.edu/Documents/in/Pascals_Wager">Pascal's Wager</a>, <script data-card-contents-for-ri="374882" type="text/json">{"id":374882,"name":"Pascal's Wager","url":"https://www.academia.edu/Documents/in/Pascals_Wager?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1032783" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Probability">Bayesian Probability</a><script data-card-contents-for-ri="1032783" type="text/json">{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=5360577]'), work: {"id":5360577,"title":"A Bayesian Analysis of Pascal's Wager for GAP.8","created_at":"2013-12-08T21:36:15.183-08:00","url":"https://www.academia.edu/5360577/A_Bayesian_Analysis_of_Pascals_Wager_for_GAP_8?f_ri=1032783","dom_id":"work_5360577","summary":null,"downloadable_attachments":[{"id":32510433,"asset_id":5360577,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":7413601,"first_name":"Stamatios","last_name":"Gerogiorgakis","domain_name":"erfurt","page_name":"StamatiosGerogiorgakis","display_name":"Stamatios Gerogiorgakis","profile_url":"https://erfurt.academia.edu/StamatiosGerogiorgakis?f_ri=1032783","photo":"https://0.academia-photos.com/7413601/3004621/3528508/s65_stamatios.gerogiorgakis.png"}],"research_interests":[{"id":902,"name":"Philosophy Of Religion","url":"https://www.academia.edu/Documents/in/Philosophy_Of_Religion?f_ri=1032783","nofollow":true},{"id":374882,"name":"Pascal's Wager","url":"https://www.academia.edu/Documents/in/Pascals_Wager?f_ri=1032783","nofollow":true},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_80353086" data-work_id="80353086" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/80353086/Biases_and_Variability_from_Costly_Bayesian_Inference">Biases and Variability from Costly Bayesian Inference</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">When humans infer underlying probabilities from stochastic observations, they exhibit biases and variability that cannot be explained on the basis of sound, Bayesian manipulations of probability. This is especially salient when beliefs... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_80353086" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">When humans infer underlying probabilities from stochastic observations, they exhibit biases and variability that cannot be explained on the basis of sound, Bayesian manipulations of probability. This is especially salient when beliefs are updated as a function of sequential observations. We introduce a theoretical framework in which biases and variability emerge from a trade-off between Bayesian inference and the cognitive cost of carrying out probabilistic computations. We consider two forms of the cost: a precision cost and an unpredictability cost; these penalize beliefs that are less entropic and less deterministic, respectively. We apply our framework to the case of a Bernoulli variable: the bias of a coin is inferred from a sequence of coin flips. Theoretical predictions are qualitatively different depending on the form of the cost. A precision cost induces overestimation of small probabilities, on average, and a limited memory of past observations, and, consequently, a fluct...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/80353086" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b9871b208bf7150d1526d795a5dbd251" rel="nofollow" data-download="{"attachment_id":86763448,"asset_id":80353086,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/86763448/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="7984266" href="https://independent.academia.edu/RavaAzeredodaSilveira">Rava Azeredo da Silveira</a><script data-card-contents-for-user="7984266" type="text/json">{"id":7984266,"first_name":"Rava","last_name":"Azeredo da Silveira","domain_name":"independent","page_name":"RavaAzeredodaSilveira","display_name":"Rava Azeredo da Silveira","profile_url":"https://independent.academia.edu/RavaAzeredodaSilveira?f_ri=1032783","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_80353086 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="80353086"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 80353086, container: ".js-paper-rank-work_80353086", }); 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This is especially salient when beliefs are updated as a function of sequential observations. We introduce a theoretical framework in which biases and variability emerge from a trade-off between Bayesian inference and the cognitive cost of carrying out probabilistic computations. We consider two forms of the cost: a precision cost and an unpredictability cost; these penalize beliefs that are less entropic and less deterministic, respectively. We apply our framework to the case of a Bernoulli variable: the bias of a coin is inferred from a sequence of coin flips. Theoretical predictions are qualitatively different depending on the form of the cost. A precision cost induces overestimation of small probabilities, on average, and a limited memory of past observations, and, consequently, a fluct...","downloadable_attachments":[{"id":86763448,"asset_id":80353086,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":7984266,"first_name":"Rava","last_name":"Azeredo da Silveira","domain_name":"independent","page_name":"RavaAzeredodaSilveira","display_name":"Rava Azeredo da Silveira","profile_url":"https://independent.academia.edu/RavaAzeredodaSilveira?f_ri=1032783","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true},{"id":7710,"name":"Biology","url":"https://www.academia.edu/Documents/in/Biology?f_ri=1032783","nofollow":true},{"id":18574,"name":"Inference","url":"https://www.academia.edu/Documents/in/Inference?f_ri=1032783","nofollow":true},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine?f_ri=1032783","nofollow":true},{"id":36265,"name":"Entropy","url":"https://www.academia.edu/Documents/in/Entropy?f_ri=1032783"},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_75015390" data-work_id="75015390" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/75015390/Small_Area_Estimation_on_Zero_Inflated_Data_Using_Frequentist_and_Bayesian_Approach">Small Area Estimation on Zero-Inflated Data Using Frequentist and Bayesian Approach</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The most commonly used method of small area estimation (SAE) is the empirical best linear unbiased prediction method based on a linear mixed model. However, it is not appropriate in the case of the zero-inflated target variable with a... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_75015390" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The most commonly used method of small area estimation (SAE) is the empirical best linear unbiased prediction method based on a linear mixed model. However, it is not appropriate in the case of the zero-inflated target variable with a mixture of zeros and continuously distributed positive values. Therefore, various model-based SAE methods for zero-inflated data are developed, such as the Frequentist approach and the Bayesian approach. Both approaches are compared with the survey regression (SR) method which ignores the presence of zero-inflation in the data. The results show that the two SAE approaches for zero-inflated data are capable to yield more accurate area mean estimates than the SR method.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/75015390" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="753086415cd7a56c2af02542d396b4ab" rel="nofollow" data-download="{"attachment_id":82956414,"asset_id":75015390,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/82956414/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="25994132" href="https://independent.academia.edu/rahmaanisa2">rahma anisa</a><script data-card-contents-for-user="25994132" type="text/json">{"id":25994132,"first_name":"rahma","last_name":"anisa","domain_name":"independent","page_name":"rahmaanisa2","display_name":"rahma anisa","profile_url":"https://independent.academia.edu/rahmaanisa2?f_ri=1032783","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_75015390 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="75015390"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 75015390, container: ".js-paper-rank-work_75015390", }); 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However, it is not appropriate in the case of the zero-inflated target variable with a mixture of zeros and continuously distributed positive values. Therefore, various model-based SAE methods for zero-inflated data are developed, such as the Frequentist approach and the Bayesian approach. Both approaches are compared with the survey regression (SR) method which ignores the presence of zero-inflation in the data. The results show that the two SAE approaches for zero-inflated data are capable to yield more accurate area mean estimates than the SR method.","downloadable_attachments":[{"id":82956414,"asset_id":75015390,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":25994132,"first_name":"rahma","last_name":"anisa","domain_name":"independent","page_name":"rahmaanisa2","display_name":"rahma anisa","profile_url":"https://independent.academia.edu/rahmaanisa2?f_ri=1032783","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=1032783","nofollow":true},{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1032783","nofollow":true},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_31135813" data-work_id="31135813" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/31135813/Literature_extract_from_V_V_Nalimov_In_the_labyrinths_of_language_a_mathematicians_journey_ISI_Press_1981">Literature extract from: V. V. Nalimov: In the labyrinths of language: a mathematician's journey ISI Press 1981</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Literature extract from V.V. Nalimov: In the labyrinths of language: a mathematician's journey, ISI Press 1981 Disclaimer: This literature extract was gathered purely and subjective according the interests of the author (Manfred... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_31135813" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Literature extract from V.V. Nalimov: In the labyrinths of language: a mathematician's journey, ISI Press 1981 <br /> <br />Disclaimer: This literature extract was gathered purely and subjective according the interests of the author (Manfred Bundschuh). Usually there were complete sentences from the original transferred. Especially the Table of Contents is my invention, elaborated 30 years after the excerption of the text, in order to facilitate the readers orientation. There's no guarantee for correctness. <br />1. R. G. Colodny: Introduction   1 <br />2. Introduction to Russian Edition   1 <br />3. What is language   2  <br />3.1 Language as hard and/or soft Structure   3 <br />3.2 Information                   4 <br />3.3 Structural Traits of Language   4 <br />3.4 Hierarchy of  Language   5 <br />3.5 A Hierarchy of Languages – Metalanguages   5 <br />4. Sign Systems   7 <br />5. Probabilistic Semantiology   8 <br />5.1 Linguistic Philosophy           8 <br />5.2 Gödel's Theorem                   9 <br />6. The Probabilistic Approach – Bayes 10 <br />6.1 The Semantic Analysis of Sign Systems 11 <br />6.2 Scientific Models and Fine Contradictions 12 <br />7. Analysis of the whole Semantic Diversity of Scientific Terminology 14 <br />8. Mathematical Language and Metamathematics 15 <br />8.1 Context-free Languages         17 <br />8.2 The Language of Physics         18 <br />8.3 Mathematization of Knowledge 18 <br />8.4 Names of Things 19 <br />9. Epilogue         20 <br />9.1 Dictionaries and Semantics 20 <br />9.2 Word Semantics         21 <br />9.3 Continuous Fields of Consciousness 22 <br /> <br />This book the 1st of three. 2nd: Faces of Science. 3rd: Realms of the Unconsciousness. A 4th book of this excellent russian scientist which I extracted is: Space, Time and Life. The Probabilistic Pathways of Evolution. <br /> <br />The book is about: What is language. Some ideas dealt with: cybernetic and linguistic standpoints, probabilistic, semantictiology, Kolmogorov, Wittgenstein, Tarski, Bally, Moore (Principia Ethica, 1903, The Conception of Reality 1917), Gödels proof, Bayes' Theorem, Chomsky, Hutten, Indian Veda, Zen Koans and more. Here are 19 pages extract from the book.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/31135813" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="86cd640be8d652e0013cf871e95d9614" rel="nofollow" data-download="{"attachment_id":63555674,"asset_id":31135813,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/63555674/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="854565" href="https://uni-koeln.academia.edu/ManfredBundschuh">Manfred Bundschuh</a><script data-card-contents-for-user="854565" type="text/json">{"id":854565,"first_name":"Manfred","last_name":"Bundschuh","domain_name":"uni-koeln","page_name":"ManfredBundschuh","display_name":"Manfred Bundschuh","profile_url":"https://uni-koeln.academia.edu/ManfredBundschuh?f_ri=1032783","photo":"https://0.academia-photos.com/854565/304194/360122/s65_manfred.bundschuh.jpg"}</script></span></span></li><li class="js-paper-rank-work_31135813 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="31135813"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 31135813, container: ".js-paper-rank-work_31135813", }); 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$(".js-view-count[data-work-id=31135813]").text(description); $(".js-view-count-work_31135813").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_31135813").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="31135813"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">13</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="807" rel="nofollow" href="https://www.academia.edu/Documents/in/Philosophy_Of_Language">Philosophy Of Language</a>, <script data-card-contents-for-ri="807" type="text/json">{"id":807,"name":"Philosophy Of Language","url":"https://www.academia.edu/Documents/in/Philosophy_Of_Language?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="815" rel="nofollow" href="https://www.academia.edu/Documents/in/Epistemology">Epistemology</a>, <script data-card-contents-for-ri="815" type="text/json">{"id":815,"name":"Epistemology","url":"https://www.academia.edu/Documents/in/Epistemology?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2055" rel="nofollow" href="https://www.academia.edu/Documents/in/Consciousness_Psychology_">Consciousness (Psychology)</a>, <script data-card-contents-for-ri="2055" type="text/json">{"id":2055,"name":"Consciousness (Psychology)","url":"https://www.academia.edu/Documents/in/Consciousness_Psychology_?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2349" rel="nofollow" href="https://www.academia.edu/Documents/in/Semantics">Semantics</a><script data-card-contents-for-ri="2349" type="text/json">{"id":2349,"name":"Semantics","url":"https://www.academia.edu/Documents/in/Semantics?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=31135813]'), work: {"id":31135813,"title":"Literature extract from: V. V. Nalimov: In the labyrinths of language: a mathematician's journey ISI Press 1981","created_at":"2017-01-31T07:01:26.145-08:00","url":"https://www.academia.edu/31135813/Literature_extract_from_V_V_Nalimov_In_the_labyrinths_of_language_a_mathematicians_journey_ISI_Press_1981?f_ri=1032783","dom_id":"work_31135813","summary":"Literature extract from V.V. Nalimov: In the labyrinths of language: a mathematician's journey, ISI Press 1981\r\n\r\nDisclaimer: This literature extract was gathered purely and subjective according the interests of the author (Manfred Bundschuh). Usually there were complete sentences from the original transferred. Especially the Table of Contents is my invention, elaborated 30 years after the excerption of the text, in order to facilitate the readers orientation. There's no guarantee for correctness.\r\n1.\tR. G. Colodny: Introduction\t\t\t\t\t\t 1\r\n2.\tIntroduction to Russian Edition\t\t\t\t\t 1\r\n3.\tWhat is language\t\t\t\t\t\t\t\t 2 \r\n3.1\tLanguage as hard and/or soft Structure\t\t\t 3\r\n3.2\tInformation\t\t\t\t\t\t\t\t 4\r\n3.3\tStructural Traits of Language\t\t\t\t\t\t 4\r\n3.4\tHierarchy of Language\t\t\t\t\t\t\t 5\r\n3.5\tA Hierarchy of Languages – Metalanguages\t\t\t 5\r\n4.\tSign Systems\t\t\t\t\t\t\t\t\t 7\r\n5.\tProbabilistic Semantiology\t\t\t\t\t\t 8\r\n5.1\tLinguistic Philosophy\t\t\t\t\t\t\t 8\r\n5.2\tGödel's Theorem\t\t\t\t\t\t\t 9\r\n6.\tThe Probabilistic Approach – Bayes\t\t\t\t 10\r\n6.1\tThe Semantic Analysis of Sign Systems\t\t\t 11\r\n6.2\tScientific Models and Fine Contradictions\t\t\t 12\r\n7.\tAnalysis of the whole Semantic Diversity of Scientific Terminology\t 14\r\n8.\tMathematical Language and Metamathematics\t\t15\r\n8.1\tContext-free Languages\t\t\t\t\t\t 17\r\n8.2\tThe Language of Physics\t\t\t\t\t\t 18\r\n8.3\tMathematization of Knowledge\t\t\t\t\t 18\r\n8.4\tNames of Things\t\t\t\t\t\t\t\t 19\r\n9.\tEpilogue\t\t\t\t\t\t\t\t\t 20\r\n9.1\tDictionaries and Semantics\t\t\t\t\t\t 20\r\n9.2\tWord Semantics\t\t\t\t\t\t\t\t 21\r\n9.3\tContinuous Fields of Consciousness\t\t\t\t 22\r\n\r\nThis book the 1st of three. 2nd: Faces of Science. 3rd: Realms of the Unconsciousness. A 4th book of this excellent russian scientist which I extracted is: Space, Time and Life. The Probabilistic Pathways of Evolution.\r\n\r\nThe book is about: What is language. Some ideas dealt with: cybernetic and linguistic standpoints, probabilistic, semantictiology, Kolmogorov, Wittgenstein, Tarski, Bally, Moore (Principia Ethica, 1903, The Conception of Reality 1917), Gödels proof, Bayes' Theorem, Chomsky, Hutten, Indian Veda, Zen Koans and more. Here are 19 pages extract from the book.","downloadable_attachments":[{"id":63555674,"asset_id":31135813,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":854565,"first_name":"Manfred","last_name":"Bundschuh","domain_name":"uni-koeln","page_name":"ManfredBundschuh","display_name":"Manfred Bundschuh","profile_url":"https://uni-koeln.academia.edu/ManfredBundschuh?f_ri=1032783","photo":"https://0.academia-photos.com/854565/304194/360122/s65_manfred.bundschuh.jpg"}],"research_interests":[{"id":807,"name":"Philosophy Of Language","url":"https://www.academia.edu/Documents/in/Philosophy_Of_Language?f_ri=1032783","nofollow":true},{"id":815,"name":"Epistemology","url":"https://www.academia.edu/Documents/in/Epistemology?f_ri=1032783","nofollow":true},{"id":2055,"name":"Consciousness (Psychology)","url":"https://www.academia.edu/Documents/in/Consciousness_Psychology_?f_ri=1032783","nofollow":true},{"id":2349,"name":"Semantics","url":"https://www.academia.edu/Documents/in/Semantics?f_ri=1032783","nofollow":true},{"id":34457,"name":"Natural Semantic Metalanguage","url":"https://www.academia.edu/Documents/in/Natural_Semantic_Metalanguage?f_ri=1032783"},{"id":34543,"name":"Gödel's Incompleteness Theorems","url":"https://www.academia.edu/Documents/in/G%C3%B6dels_Incompleteness_Theorems?f_ri=1032783"},{"id":38949,"name":"Mathematical Language","url":"https://www.academia.edu/Documents/in/Mathematical_Language?f_ri=1032783"},{"id":70888,"name":"Erkenntnistheorie","url":"https://www.academia.edu/Documents/in/Erkenntnistheorie?f_ri=1032783"},{"id":88453,"name":"Metalanguage","url":"https://www.academia.edu/Documents/in/Metalanguage?f_ri=1032783"},{"id":265953,"name":"Linguistics and Philosophy","url":"https://www.academia.edu/Documents/in/Linguistics_and_Philosophy?f_ri=1032783"},{"id":721682,"name":"Erkenntnis","url":"https://www.academia.edu/Documents/in/Erkenntnis?f_ri=1032783"},{"id":982612,"name":"Ludwig Wittgenstein's Philosophy of Language","url":"https://www.academia.edu/Documents/in/Ludwig_Wittgensteins_Philosophy_of_Language?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_5668195" data-work_id="5668195" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/5668195/_Bayesian_Theism_and_the_Interpretation_of_Bayesian_Probabilities_in_Ramelow_A_2014_God_Munich_Philosophia_uncorrected_printers_proofs_typos_in_formalism_on_pp_129_130_">"Bayesian Theism and the Interpretation of Bayesian Probabilities" in Ramelow, A. (2014), God, Munich: Philosophia (uncorrected printer's proofs - typos in formalism on pp. 129-130)</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/5668195" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4a929c1ae9ff7ca860ccdc2fcb8c9191" rel="nofollow" data-download="{"attachment_id":32722608,"asset_id":5668195,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/32722608/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="7413601" href="https://erfurt.academia.edu/StamatiosGerogiorgakis">Stamatios Gerogiorgakis</a><script data-card-contents-for-user="7413601" type="text/json">{"id":7413601,"first_name":"Stamatios","last_name":"Gerogiorgakis","domain_name":"erfurt","page_name":"StamatiosGerogiorgakis","display_name":"Stamatios Gerogiorgakis","profile_url":"https://erfurt.academia.edu/StamatiosGerogiorgakis?f_ri=1032783","photo":"https://0.academia-photos.com/7413601/3004621/3528508/s65_stamatios.gerogiorgakis.png"}</script></span></span></li><li class="js-paper-rank-work_5668195 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="5668195"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 5668195, container: ".js-paper-rank-work_5668195", }); 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$(".js-view-count[data-work-id=5668195]").text(description); $(".js-view-count-work_5668195").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_5668195").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="5668195"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="902" rel="nofollow" href="https://www.academia.edu/Documents/in/Philosophy_Of_Religion">Philosophy Of Religion</a>, <script data-card-contents-for-ri="902" type="text/json">{"id":902,"name":"Philosophy Of Religion","url":"https://www.academia.edu/Documents/in/Philosophy_Of_Religion?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="83385" rel="nofollow" href="https://www.academia.edu/Documents/in/Richard_Swinburne">Richard Swinburne</a>, <script data-card-contents-for-ri="83385" type="text/json">{"id":83385,"name":"Richard Swinburne","url":"https://www.academia.edu/Documents/in/Richard_Swinburne?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="164245" rel="nofollow" href="https://www.academia.edu/Documents/in/Subjective_Probability">Subjective Probability</a>, <script data-card-contents-for-ri="164245" type="text/json">{"id":164245,"name":"Subjective Probability","url":"https://www.academia.edu/Documents/in/Subjective_Probability?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="736335" rel="nofollow" href="https://www.academia.edu/Documents/in/Analytical_Philosophy_of_Religion">Analytical Philosophy of Religion</a><script data-card-contents-for-ri="736335" type="text/json">{"id":736335,"name":"Analytical Philosophy of Religion","url":"https://www.academia.edu/Documents/in/Analytical_Philosophy_of_Religion?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=5668195]'), work: {"id":5668195,"title":"\"Bayesian Theism and the Interpretation of Bayesian Probabilities\" in Ramelow, A. (2014), God, Munich: Philosophia (uncorrected printer's proofs - typos in formalism on pp. 129-130)","created_at":"2014-01-10T00:04:24.108-08:00","url":"https://www.academia.edu/5668195/_Bayesian_Theism_and_the_Interpretation_of_Bayesian_Probabilities_in_Ramelow_A_2014_God_Munich_Philosophia_uncorrected_printers_proofs_typos_in_formalism_on_pp_129_130_?f_ri=1032783","dom_id":"work_5668195","summary":null,"downloadable_attachments":[{"id":32722608,"asset_id":5668195,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":7413601,"first_name":"Stamatios","last_name":"Gerogiorgakis","domain_name":"erfurt","page_name":"StamatiosGerogiorgakis","display_name":"Stamatios Gerogiorgakis","profile_url":"https://erfurt.academia.edu/StamatiosGerogiorgakis?f_ri=1032783","photo":"https://0.academia-photos.com/7413601/3004621/3528508/s65_stamatios.gerogiorgakis.png"}],"research_interests":[{"id":902,"name":"Philosophy Of Religion","url":"https://www.academia.edu/Documents/in/Philosophy_Of_Religion?f_ri=1032783","nofollow":true},{"id":83385,"name":"Richard Swinburne","url":"https://www.academia.edu/Documents/in/Richard_Swinburne?f_ri=1032783","nofollow":true},{"id":164245,"name":"Subjective Probability","url":"https://www.academia.edu/Documents/in/Subjective_Probability?f_ri=1032783","nofollow":true},{"id":736335,"name":"Analytical Philosophy of Religion","url":"https://www.academia.edu/Documents/in/Analytical_Philosophy_of_Religion?f_ri=1032783","nofollow":true},{"id":753483,"name":"Bayesian Confirmation Theory","url":"https://www.academia.edu/Documents/in/Bayesian_Confirmation_Theory?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_82174765" data-work_id="82174765" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/82174765/Multi_Fidelity_Bayesian_Approach_for_History_Matching_in_Reservoir_Simulation">Multi-Fidelity Bayesian Approach for History Matching in Reservoir Simulation</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">History matching is a critical step within the reservoir management process to synchronize the simulation model with the production data. The history-matched model can be used for planning optimum field development and performing... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_82174765" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">History matching is a critical step within the reservoir management process to synchronize the simulation model with the production data. The history-matched model can be used for planning optimum field development and performing optimization and uncertainty quantifications. We present a novel history matching workflow based on a Bayesian framework that accommodates subsurface uncertainties. Our workflow involves three different model resolutions within the Bayesian framework: 1) a coarse low-fidelity model to update the prior range, 2) a fine low-fidelity model to represent the high-fidelity model, and 3) a high-fidelity model to re-construct the real response. The low-fidelity model is constructed by a multivariate polynomial function, while the high-fidelity model is based on the reservoir simulation model. We firstly develop a coarse low-fidelity model using a two-level Design of Experiment (DoE), which aims to provide a better prior. We secondly use Latin Hypercube Sampling (LH...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/82174765" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b753d1ec4d3d80e6223ce0a4f2e9c297" rel="nofollow" data-download="{"attachment_id":87964558,"asset_id":82174765,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/87964558/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="114952807" href="https://independent.academia.edu/HHoteit">Hussein Hoteit</a><script data-card-contents-for-user="114952807" type="text/json">{"id":114952807,"first_name":"Hussein","last_name":"Hoteit","domain_name":"independent","page_name":"HHoteit","display_name":"Hussein Hoteit","profile_url":"https://independent.academia.edu/HHoteit?f_ri=1032783","photo":"https://0.academia-photos.com/114952807/46492490/35909127/s65_hussein.hoteit.jpg"}</script></span></span></li><li class="js-paper-rank-work_82174765 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="82174765"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 82174765, container: ".js-paper-rank-work_82174765", }); });</script></li><li class="js-percentile-work_82174765 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 82174765; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_82174765"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_82174765 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="82174765"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 82174765; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=82174765]").text(description); $(".js-view-count-work_82174765").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_82174765").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="82174765"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="96858" rel="nofollow" href="https://www.academia.edu/Documents/in/Reservoir_Computing">Reservoir Computing</a>, <script data-card-contents-for-ri="96858" type="text/json">{"id":96858,"name":"Reservoir Computing","url":"https://www.academia.edu/Documents/in/Reservoir_Computing?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1032783" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Probability">Bayesian Probability</a><script data-card-contents-for-ri="1032783" type="text/json">{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=82174765]'), work: {"id":82174765,"title":"Multi-Fidelity Bayesian Approach for History Matching in Reservoir Simulation","created_at":"2022-06-24T10:41:09.962-07:00","url":"https://www.academia.edu/82174765/Multi_Fidelity_Bayesian_Approach_for_History_Matching_in_Reservoir_Simulation?f_ri=1032783","dom_id":"work_82174765","summary":"History matching is a critical step within the reservoir management process to synchronize the simulation model with the production data. The history-matched model can be used for planning optimum field development and performing optimization and uncertainty quantifications. We present a novel history matching workflow based on a Bayesian framework that accommodates subsurface uncertainties. Our workflow involves three different model resolutions within the Bayesian framework: 1) a coarse low-fidelity model to update the prior range, 2) a fine low-fidelity model to represent the high-fidelity model, and 3) a high-fidelity model to re-construct the real response. The low-fidelity model is constructed by a multivariate polynomial function, while the high-fidelity model is based on the reservoir simulation model. We firstly develop a coarse low-fidelity model using a two-level Design of Experiment (DoE), which aims to provide a better prior. We secondly use Latin Hypercube Sampling (LH...","downloadable_attachments":[{"id":87964558,"asset_id":82174765,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":114952807,"first_name":"Hussein","last_name":"Hoteit","domain_name":"independent","page_name":"HHoteit","display_name":"Hussein Hoteit","profile_url":"https://independent.academia.edu/HHoteit?f_ri=1032783","photo":"https://0.academia-photos.com/114952807/46492490/35909127/s65_hussein.hoteit.jpg"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true},{"id":96858,"name":"Reservoir Computing","url":"https://www.academia.edu/Documents/in/Reservoir_Computing?f_ri=1032783","nofollow":true},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_82081483" data-work_id="82081483" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/82081483/Bayesian_Posterior_Odds_Ratios">Bayesian Posterior Odds Ratios</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">To begin statistical analysis, Bayesians quantify their confidence in modeling hypotheses with priors. A prior describes the probability of a certain modeling hypothesis apart from the data. Bayesians should be able to defend their choice... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_82081483" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">To begin statistical analysis, Bayesians quantify their confidence in modeling hypotheses with priors. A prior describes the probability of a certain modeling hypothesis apart from the data. Bayesians should be able to defend their choice of prior to a skeptical audience. Collaboration between evaluators and stakeholders could make their choices more defensible. This article describes how evaluators and stakeholders could combine their expertise to select rigorous priors for analysis. The article first introduces Bayesian testing, then situates it within a collaborative framework, and finally illustrates the method with a real example.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/82081483" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ca65df3ca5e6572487252eac7a5ee2b7" rel="nofollow" data-download="{"attachment_id":87900967,"asset_id":82081483,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/87900967/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="50913107" href="https://independent.academia.edu/HoonChoi4">Jeong Hoon Choi</a><script data-card-contents-for-user="50913107" type="text/json">{"id":50913107,"first_name":"Jeong Hoon","last_name":"Choi","domain_name":"independent","page_name":"HoonChoi4","display_name":"Jeong Hoon Choi","profile_url":"https://independent.academia.edu/HoonChoi4?f_ri=1032783","photo":"https://0.academia-photos.com/50913107/31986867/29117948/s65_hoon.choi.jpg"}</script></span></span></li><li class="js-paper-rank-work_82081483 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="82081483"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 82081483, container: ".js-paper-rank-work_82081483", }); 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$(".js-view-count[data-work-id=82081483]").text(description); $(".js-view-count-work_82081483").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_82081483").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="82081483"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="73149" rel="nofollow" href="https://www.academia.edu/Documents/in/Business_and_Management">Business and Management</a>, <script data-card-contents-for-ri="73149" type="text/json">{"id":73149,"name":"Business and Management","url":"https://www.academia.edu/Documents/in/Business_and_Management?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1032783" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Probability">Bayesian Probability</a><script data-card-contents-for-ri="1032783" type="text/json">{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=82081483]'), work: {"id":82081483,"title":"Bayesian Posterior Odds Ratios","created_at":"2022-06-22T15:15:40.725-07:00","url":"https://www.academia.edu/82081483/Bayesian_Posterior_Odds_Ratios?f_ri=1032783","dom_id":"work_82081483","summary":"To begin statistical analysis, Bayesians quantify their confidence in modeling hypotheses with priors. A prior describes the probability of a certain modeling hypothesis apart from the data. Bayesians should be able to defend their choice of prior to a skeptical audience. Collaboration between evaluators and stakeholders could make their choices more defensible. This article describes how evaluators and stakeholders could combine their expertise to select rigorous priors for analysis. The article first introduces Bayesian testing, then situates it within a collaborative framework, and finally illustrates the method with a real example.","downloadable_attachments":[{"id":87900967,"asset_id":82081483,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":50913107,"first_name":"Jeong Hoon","last_name":"Choi","domain_name":"independent","page_name":"HoonChoi4","display_name":"Jeong Hoon Choi","profile_url":"https://independent.academia.edu/HoonChoi4?f_ri=1032783","photo":"https://0.academia-photos.com/50913107/31986867/29117948/s65_hoon.choi.jpg"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true},{"id":73149,"name":"Business and Management","url":"https://www.academia.edu/Documents/in/Business_and_Management?f_ri=1032783","nofollow":true},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_81944653" data-work_id="81944653" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/81944653/The_time_of_calla%C3%AFs_radiocarbon_dates_and_Bayesian_chronological_modelling">The time of callaïs: radiocarbon dates and Bayesian chronological modelling</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Research on the provenance of rare green stone materials has produced new insights into the value systems of societies in western and central Europe between the 6th and 3rd millennia cal BC. This contribution presents the results of a... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_81944653" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Research on the provenance of rare green stone materials has produced new insights into the value systems of societies in western and central Europe between the 6th and 3rd millennia cal BC. This contribution presents the results of a Bayesian statistical analysis of 406 current available radiocarbon results from variscite and turquoise (callais) contexts in Europe, along with the results of provenance analyses, undertaken to investigate the fine-grained temporal pattern for the exploitation, circulation and deposition of callais artifacts.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/81944653" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d871a23fc9d952967f38a75119e3d399" rel="nofollow" data-download="{"attachment_id":87811620,"asset_id":81944653,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/87811620/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="3262064" href="https://univ-tlse2.academia.edu/jeanVaquer">jean Vaquer</a><script data-card-contents-for-user="3262064" type="text/json">{"id":3262064,"first_name":"jean","last_name":"Vaquer","domain_name":"univ-tlse2","page_name":"jeanVaquer","display_name":"jean Vaquer","profile_url":"https://univ-tlse2.academia.edu/jeanVaquer?f_ri=1032783","photo":"https://0.academia-photos.com/3262064/2078807/2446048/s65_jean.vaquer.jpg"}</script></span></span></li><li class="js-paper-rank-work_81944653 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="81944653"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 81944653, container: ".js-paper-rank-work_81944653", }); 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$(".js-view-count[data-work-id=81944653]").text(description); $(".js-view-count-work_81944653").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_81944653").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="81944653"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">11</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="406" rel="nofollow" href="https://www.academia.edu/Documents/in/Geology">Geology</a>, <script data-card-contents-for-ri="406" type="text/json">{"id":406,"name":"Geology","url":"https://www.academia.edu/Documents/in/Geology?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="26086" rel="nofollow" href="https://www.academia.edu/Documents/in/Neolithic_Europe">Neolithic Europe</a>, <script data-card-contents-for-ri="26086" type="text/json">{"id":26086,"name":"Neolithic Europe","url":"https://www.academia.edu/Documents/in/Neolithic_Europe?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="38701" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Analysis">Bayesian Analysis</a>, <script data-card-contents-for-ri="38701" type="text/json">{"id":38701,"name":"Bayesian Analysis","url":"https://www.academia.edu/Documents/in/Bayesian_Analysis?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="75826" rel="nofollow" href="https://www.academia.edu/Documents/in/Europe">Europe</a><script data-card-contents-for-ri="75826" type="text/json">{"id":75826,"name":"Europe","url":"https://www.academia.edu/Documents/in/Europe?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=81944653]'), work: {"id":81944653,"title":"The time of callaïs: radiocarbon dates and Bayesian chronological modelling","created_at":"2022-06-20T20:42:20.183-07:00","url":"https://www.academia.edu/81944653/The_time_of_calla%C3%AFs_radiocarbon_dates_and_Bayesian_chronological_modelling?f_ri=1032783","dom_id":"work_81944653","summary":"Research on the provenance of rare green stone materials has produced new insights into the value systems of societies in western and central Europe between the 6th and 3rd millennia cal BC. This contribution presents the results of a Bayesian statistical analysis of 406 current available radiocarbon results from variscite and turquoise (callais) contexts in Europe, along with the results of provenance analyses, undertaken to investigate the fine-grained temporal pattern for the exploitation, circulation and deposition of callais artifacts.","downloadable_attachments":[{"id":87811620,"asset_id":81944653,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":3262064,"first_name":"jean","last_name":"Vaquer","domain_name":"univ-tlse2","page_name":"jeanVaquer","display_name":"jean Vaquer","profile_url":"https://univ-tlse2.academia.edu/jeanVaquer?f_ri=1032783","photo":"https://0.academia-photos.com/3262064/2078807/2446048/s65_jean.vaquer.jpg"}],"research_interests":[{"id":406,"name":"Geology","url":"https://www.academia.edu/Documents/in/Geology?f_ri=1032783","nofollow":true},{"id":26086,"name":"Neolithic Europe","url":"https://www.academia.edu/Documents/in/Neolithic_Europe?f_ri=1032783","nofollow":true},{"id":38701,"name":"Bayesian Analysis","url":"https://www.academia.edu/Documents/in/Bayesian_Analysis?f_ri=1032783","nofollow":true},{"id":75826,"name":"Europe","url":"https://www.academia.edu/Documents/in/Europe?f_ri=1032783","nofollow":true},{"id":93937,"name":"Radiocarbon Dating","url":"https://www.academia.edu/Documents/in/Radiocarbon_Dating?f_ri=1032783"},{"id":230462,"name":"LA CALAÍTA EN EUROPA","url":"https://www.academia.edu/Documents/in/LA_CALAITA_EN_EUROPA?f_ri=1032783"},{"id":236376,"name":"Radiocarbon Dates","url":"https://www.academia.edu/Documents/in/Radiocarbon_Dates?f_ri=1032783"},{"id":255381,"name":"Turquoise","url":"https://www.academia.edu/Documents/in/Turquoise?f_ri=1032783"},{"id":543872,"name":"Variscite","url":"https://www.academia.edu/Documents/in/Variscite?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"},{"id":3390320,"name":"Callaïs","url":"https://www.academia.edu/Documents/in/Calla%C3%AFs?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_81156623" data-work_id="81156623" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/81156623/Bayesian_methods_for_completing_data_in_space_time_panel_models_I">Bayesian methods for completing data in space-time panel models I</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Completing data sets that are collected in heterogeneous units is a quite frequent problem. Chow and Lin (1971) were the rst to develop a unied framework for the three problems (interpolation, extrapolation and distribution) of predicting... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_81156623" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Completing data sets that are collected in heterogeneous units is a quite frequent problem. Chow and Lin (1971) were the rst to develop a unied framework for the three problems (interpolation, extrapolation and distribution) of predicting times series by related series (the ‘indicators’). This paper develops a spatial Chow-Lin procedure for cross-sectional and panel data and compares the classical and Bayesian estimation methods. We outline the error covariance structure in a spatial context and derive the BLUE for the ML and Bayesian MCMC estimation. Finally, we apply the procedure to Spanish regional GDP data between 2000-2004. We assume that only NUTS-2 GDP is known and predict GDP at NUTS-3 level by using socio-economic and spatial information available at NUTS-3. The spatial neighborhood is dened by either km distance, travel time, contiguity and trade relationships. After running some sensitivity analysis, we present the forecast accuracy criteria comparing the predicted value...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/81156623" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d122d9c8d66551e8218e8876d29a30ff" rel="nofollow" data-download="{"attachment_id":87299522,"asset_id":81156623,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/87299522/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="29102771" href="https://uam.academia.edu/CarlosLlano">Carlos Llano</a><script data-card-contents-for-user="29102771" type="text/json">{"id":29102771,"first_name":"Carlos","last_name":"Llano","domain_name":"uam","page_name":"CarlosLlano","display_name":"Carlos Llano","profile_url":"https://uam.academia.edu/CarlosLlano?f_ri=1032783","photo":"https://0.academia-photos.com/29102771/8294843/9277814/s65_carlos.llano.jpg"}</script></span></span></li><li class="js-paper-rank-work_81156623 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="81156623"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 81156623, container: ".js-paper-rank-work_81156623", }); 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Chow and Lin (1971) were the rst to develop a unied framework for the three problems (interpolation, extrapolation and distribution) of predicting times series by related series (the ‘indicators’). This paper develops a spatial Chow-Lin procedure for cross-sectional and panel data and compares the classical and Bayesian estimation methods. We outline the error covariance structure in a spatial context and derive the BLUE for the ML and Bayesian MCMC estimation. Finally, we apply the procedure to Spanish regional GDP data between 2000-2004. We assume that only NUTS-2 GDP is known and predict GDP at NUTS-3 level by using socio-economic and spatial information available at NUTS-3. The spatial neighborhood is dened by either km distance, travel time, contiguity and trade relationships. After running some sensitivity analysis, we present the forecast accuracy criteria comparing the predicted value...","downloadable_attachments":[{"id":87299522,"asset_id":81156623,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":29102771,"first_name":"Carlos","last_name":"Llano","domain_name":"uam","page_name":"CarlosLlano","display_name":"Carlos Llano","profile_url":"https://uam.academia.edu/CarlosLlano?f_ri=1032783","photo":"https://0.academia-photos.com/29102771/8294843/9277814/s65_carlos.llano.jpg"}],"research_interests":[{"id":261,"name":"Geography","url":"https://www.academia.edu/Documents/in/Geography?f_ri=1032783","nofollow":true},{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=1032783","nofollow":true},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true},{"id":4390,"name":"MCMC","url":"https://www.academia.edu/Documents/in/MCMC?f_ri=1032783","nofollow":true},{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=1032783"},{"id":20099,"name":"Sensitivity Analysis","url":"https://www.academia.edu/Documents/in/Sensitivity_Analysis?f_ri=1032783"},{"id":40860,"name":"Panel Data","url":"https://www.academia.edu/Documents/in/Panel_Data?f_ri=1032783"},{"id":105608,"name":"Spatial","url":"https://www.academia.edu/Documents/in/Spatial?f_ri=1032783"},{"id":138520,"name":"Spatial Information","url":"https://www.academia.edu/Documents/in/Spatial_Information?f_ri=1032783"},{"id":154543,"name":"Space Time","url":"https://www.academia.edu/Documents/in/Space_Time?f_ri=1032783"},{"id":254570,"name":"Interpolation","url":"https://www.academia.edu/Documents/in/Interpolation?f_ri=1032783"},{"id":404000,"name":"Cross Section","url":"https://www.academia.edu/Documents/in/Cross_Section?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"},{"id":2511606,"name":"spatial context","url":"https://www.academia.edu/Documents/in/spatial_context?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_74156218" data-work_id="74156218" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/74156218/Adaptive_multi_class_Bayesian_sparse_regression_An_application_to_brain_activity_classification">Adaptive multi-class Bayesian sparse regression-An application to brain activity classification</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this article we describe a novel method for regularized regression and apply it to the prediction of a behavioural variable from brain activation images. In the context of neuroimaging, regression or classification techniques are often... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_74156218" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this article we describe a novel method for regularized regression and apply it to the prediction of a behavioural variable from brain activation images. In the context of neuroimaging, regression or classification techniques are often plagued with the curse of dimensionality, due to the extremely high number of voxels and the limited number of activation maps. A commonly-used solution is the regularization of the weights used in the parametric prediction function. It entails the difficult issue of introducing an adapted amount of regularization in the model; this question can be addressed in a Bayesian framework, but model specification needs a careful design to balance adaptiveness and sparsity. Thus, we introduce an adaptive multi-class regularization to deal with this cluster-based structure of the data. Based on a hierarchical model and estimated in a Variational Bayes framework, our algorithm is robust to overfit and more adaptive than other regularization methods. Results on simulated data and preliminary results on real data show the accuracy of the method in the context of brain activation images.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/74156218" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="61fdffca0ca8e6638542c8df94e681f2" rel="nofollow" data-download="{"attachment_id":83655680,"asset_id":74156218,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/83655680/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="30995421" rel="nofollow" href="https://independent.academia.edu/BertrandThirion">Bertrand Thirion</a><script data-card-contents-for-user="30995421" type="text/json">{"id":30995421,"first_name":"Bertrand","last_name":"Thirion","domain_name":"independent","page_name":"BertrandThirion","display_name":"Bertrand Thirion","profile_url":"https://independent.academia.edu/BertrandThirion?f_ri=1032783","photo":"https://0.academia-photos.com/30995421/103328174/92503067/s65_bertrand.thirion.png"}</script></span></span></li><li class="js-paper-rank-work_74156218 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="74156218"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 74156218, container: ".js-paper-rank-work_74156218", }); 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In the context of neuroimaging, regression or classification techniques are often plagued with the curse of dimensionality, due to the extremely high number of voxels and the limited number of activation maps. A commonly-used solution is the regularization of the weights used in the parametric prediction function. It entails the difficult issue of introducing an adapted amount of regularization in the model; this question can be addressed in a Bayesian framework, but model specification needs a careful design to balance adaptiveness and sparsity. Thus, we introduce an adaptive multi-class regularization to deal with this cluster-based structure of the data. Based on a hierarchical model and estimated in a Variational Bayes framework, our algorithm is robust to overfit and more adaptive than other regularization methods. Results on simulated data and preliminary results on real data show the accuracy of the method in the context of brain activation images.","downloadable_attachments":[{"id":83655680,"asset_id":74156218,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":30995421,"first_name":"Bertrand","last_name":"Thirion","domain_name":"independent","page_name":"BertrandThirion","display_name":"Bertrand Thirion","profile_url":"https://independent.academia.edu/BertrandThirion?f_ri=1032783","photo":"https://0.academia-photos.com/30995421/103328174/92503067/s65_bertrand.thirion.png"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=1032783","nofollow":true},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_72185477" data-work_id="72185477" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/72185477/A_Normative_Theory_of_Argument_Strength">A Normative Theory of Argument Strength</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this article, we argue for the general importance of normative theories of argument strength. We also provide some evidence based on our recent work on the fallacies as to why Bayesian probability might, in fact, be able to supply such... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_72185477" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this article, we argue for the general importance of normative theories of argument strength. We also provide some evidence based on our recent work on the fallacies as to why Bayesian probability might, in fact, be able to supply such an account. In the remainder of the article we discuss the general characteristics that make a specifically Bayesian approach desirable, and critically evaluate putative flaws of Bayesian probability that have been raised in the argumentation literature.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/72185477" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4335bb4f3065ae76f18b882664b73a2f" rel="nofollow" data-download="{"attachment_id":81214900,"asset_id":72185477,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/81214900/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="107307424" href="https://independent.academia.edu/MOaksford">Mike Oaksford</a><script data-card-contents-for-user="107307424" type="text/json">{"id":107307424,"first_name":"Mike","last_name":"Oaksford","domain_name":"independent","page_name":"MOaksford","display_name":"Mike Oaksford","profile_url":"https://independent.academia.edu/MOaksford?f_ri=1032783","photo":"https://0.academia-photos.com/107307424/55775990/43961676/s65_mike.oaksford.jpg"}</script></span></span></li><li class="js-paper-rank-work_72185477 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="72185477"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 72185477, container: ".js-paper-rank-work_72185477", }); });</script></li><li class="js-percentile-work_72185477 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 72185477; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_72185477"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_72185477 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="72185477"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 72185477; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=72185477]").text(description); $(".js-view-count-work_72185477").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_72185477").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="72185477"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="184" rel="nofollow" href="https://www.academia.edu/Documents/in/Sociology">Sociology</a>, <script data-card-contents-for-ri="184" type="text/json">{"id":184,"name":"Sociology","url":"https://www.academia.edu/Documents/in/Sociology?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="803" rel="nofollow" href="https://www.academia.edu/Documents/in/Philosophy">Philosophy</a>, <script data-card-contents-for-ri="803" type="text/json">{"id":803,"name":"Philosophy","url":"https://www.academia.edu/Documents/in/Philosophy?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="8612" rel="nofollow" href="https://www.academia.edu/Documents/in/Argumentation">Argumentation</a>, <script data-card-contents-for-ri="8612" type="text/json">{"id":8612,"name":"Argumentation","url":"https://www.academia.edu/Documents/in/Argumentation?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="33324" rel="nofollow" href="https://www.academia.edu/Documents/in/Informal_Logic">Informal Logic</a><script data-card-contents-for-ri="33324" type="text/json">{"id":33324,"name":"Informal Logic","url":"https://www.academia.edu/Documents/in/Informal_Logic?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=72185477]'), work: {"id":72185477,"title":"A Normative Theory of Argument Strength","created_at":"2022-02-22T00:00:58.246-08:00","url":"https://www.academia.edu/72185477/A_Normative_Theory_of_Argument_Strength?f_ri=1032783","dom_id":"work_72185477","summary":"In this article, we argue for the general importance of normative theories of argument strength. We also provide some evidence based on our recent work on the fallacies as to why Bayesian probability might, in fact, be able to supply such an account. In the remainder of the article we discuss the general characteristics that make a specifically Bayesian approach desirable, and critically evaluate putative flaws of Bayesian probability that have been raised in the argumentation literature.","downloadable_attachments":[{"id":81214900,"asset_id":72185477,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":107307424,"first_name":"Mike","last_name":"Oaksford","domain_name":"independent","page_name":"MOaksford","display_name":"Mike Oaksford","profile_url":"https://independent.academia.edu/MOaksford?f_ri=1032783","photo":"https://0.academia-photos.com/107307424/55775990/43961676/s65_mike.oaksford.jpg"}],"research_interests":[{"id":184,"name":"Sociology","url":"https://www.academia.edu/Documents/in/Sociology?f_ri=1032783","nofollow":true},{"id":803,"name":"Philosophy","url":"https://www.academia.edu/Documents/in/Philosophy?f_ri=1032783","nofollow":true},{"id":8612,"name":"Argumentation","url":"https://www.academia.edu/Documents/in/Argumentation?f_ri=1032783","nofollow":true},{"id":33324,"name":"Informal Logic","url":"https://www.academia.edu/Documents/in/Informal_Logic?f_ri=1032783","nofollow":true},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_82046466" data-work_id="82046466" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/82046466/_The_1988_Wald_Memorial_Lectures_The_Present_Position_in_Bayesian_Statistics_Comment">[The 1988 Wald Memorial Lectures: The Present Position in Bayesian Statistics]: Comment</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/82046466" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ea95b80d2d2f54b9c39e2fc49639b309" rel="nofollow" data-download="{"attachment_id":87878046,"asset_id":82046466,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/87878046/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="35641077" href="https://uclouvain.academia.edu/MMouchart">M. Mouchart</a><script data-card-contents-for-user="35641077" type="text/json">{"id":35641077,"first_name":"M.","last_name":"Mouchart","domain_name":"uclouvain","page_name":"MMouchart","display_name":"M. Mouchart","profile_url":"https://uclouvain.academia.edu/MMouchart?f_ri=1032783","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_82046466 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="82046466"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 82046466, container: ".js-paper-rank-work_82046466", }); });</script></li><li class="js-percentile-work_82046466 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 82046466; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_82046466"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_82046466 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="82046466"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 82046466; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=82046466]").text(description); $(".js-view-count-work_82046466").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_82046466").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="82046466"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="892" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistics">Statistics</a>, <script data-card-contents-for-ri="892" type="text/json">{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1032783" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Probability">Bayesian Probability</a>, <script data-card-contents-for-ri="1032783" type="text/json">{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1856370" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Science">Statistical Science</a><script data-card-contents-for-ri="1856370" type="text/json">{"id":1856370,"name":"Statistical Science","url":"https://www.academia.edu/Documents/in/Statistical_Science?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=82046466]'), work: {"id":82046466,"title":"[The 1988 Wald Memorial Lectures: The Present Position in Bayesian Statistics]: Comment","created_at":"2022-06-22T03:41:55.249-07:00","url":"https://www.academia.edu/82046466/_The_1988_Wald_Memorial_Lectures_The_Present_Position_in_Bayesian_Statistics_Comment?f_ri=1032783","dom_id":"work_82046466","summary":null,"downloadable_attachments":[{"id":87878046,"asset_id":82046466,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":35641077,"first_name":"M.","last_name":"Mouchart","domain_name":"uclouvain","page_name":"MMouchart","display_name":"M. Mouchart","profile_url":"https://uclouvain.academia.edu/MMouchart?f_ri=1032783","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1032783","nofollow":true},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true},{"id":1856370,"name":"Statistical Science","url":"https://www.academia.edu/Documents/in/Statistical_Science?f_ri=1032783","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_81838673" data-work_id="81838673" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/81838673/Are_Narrow_Focus_Exhaustivity_Inferences_Bayesian_Inferences">Are Narrow Focus Exhaustivity Inferences Bayesian Inferences?</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In successful communication, the literal meaning of linguistic utterances is often enriched by pragmatic inferences. Part of the pragmatic reasoning underlying such inferences has been successfully modeled as Bayesian goal recognition in... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_81838673" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In successful communication, the literal meaning of linguistic utterances is often enriched by pragmatic inferences. Part of the pragmatic reasoning underlying such inferences has been successfully modeled as Bayesian goal recognition in the Rational Speech Act (RSA) framework. In this paper, we try to model the interpretation of question-answer sequences with narrow focus in the answer in the RSA framework, thereby exploring the effects of domain size and prior probabilities on interpretation. Should narrow focus exhaustivity inferences be actually based on Bayesian inference involving prior probabilities of states, RSA models should predict a dependency of exhaustivity on these factors. We present experimental data that suggest that interlocutors do not act according to the predictions of the RSA model and that exhaustivity is in fact approximately constant across different domain sizes and priors. The results constitute a conceptual challenge for Bayesian accounts of the underlyi...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/81838673" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="fe706fddf743496effeae69f4d60b29a" rel="nofollow" data-download="{"attachment_id":87743359,"asset_id":81838673,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/87743359/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="125488380" href="https://kfunigraz.academia.edu/EdgarOnea">Edgar Onea</a><script data-card-contents-for-user="125488380" type="text/json">{"id":125488380,"first_name":"Edgar","last_name":"Onea","domain_name":"kfunigraz","page_name":"EdgarOnea","display_name":"Edgar Onea","profile_url":"https://kfunigraz.academia.edu/EdgarOnea?f_ri=1032783","photo":"https://0.academia-photos.com/125488380/31879319/29067095/s65_edgar.onea.jpg"}</script></span></span></li><li class="js-paper-rank-work_81838673 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="81838673"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 81838673, container: ".js-paper-rank-work_81838673", }); });</script></li><li class="js-percentile-work_81838673 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 81838673; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_81838673"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_81838673 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="81838673"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 81838673; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=81838673]").text(description); $(".js-view-count-work_81838673").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_81838673").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="81838673"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="221" rel="nofollow" href="https://www.academia.edu/Documents/in/Psychology">Psychology</a>, <script data-card-contents-for-ri="221" type="text/json">{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="18574" rel="nofollow" href="https://www.academia.edu/Documents/in/Inference">Inference</a>, <script data-card-contents-for-ri="18574" type="text/json">{"id":18574,"name":"Inference","url":"https://www.academia.edu/Documents/in/Inference?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="26327" rel="nofollow" href="https://www.academia.edu/Documents/in/Medicine">Medicine</a>, <script data-card-contents-for-ri="26327" type="text/json">{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="51529" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Inference">Bayesian Inference</a><script data-card-contents-for-ri="51529" type="text/json">{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=81838673]'), work: {"id":81838673,"title":"Are Narrow Focus Exhaustivity Inferences Bayesian Inferences?","created_at":"2022-06-19T11:45:09.807-07:00","url":"https://www.academia.edu/81838673/Are_Narrow_Focus_Exhaustivity_Inferences_Bayesian_Inferences?f_ri=1032783","dom_id":"work_81838673","summary":"In successful communication, the literal meaning of linguistic utterances is often enriched by pragmatic inferences. Part of the pragmatic reasoning underlying such inferences has been successfully modeled as Bayesian goal recognition in the Rational Speech Act (RSA) framework. In this paper, we try to model the interpretation of question-answer sequences with narrow focus in the answer in the RSA framework, thereby exploring the effects of domain size and prior probabilities on interpretation. Should narrow focus exhaustivity inferences be actually based on Bayesian inference involving prior probabilities of states, RSA models should predict a dependency of exhaustivity on these factors. We present experimental data that suggest that interlocutors do not act according to the predictions of the RSA model and that exhaustivity is in fact approximately constant across different domain sizes and priors. The results constitute a conceptual challenge for Bayesian accounts of the underlyi...","downloadable_attachments":[{"id":87743359,"asset_id":81838673,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":125488380,"first_name":"Edgar","last_name":"Onea","domain_name":"kfunigraz","page_name":"EdgarOnea","display_name":"Edgar Onea","profile_url":"https://kfunigraz.academia.edu/EdgarOnea?f_ri=1032783","photo":"https://0.academia-photos.com/125488380/31879319/29067095/s65_edgar.onea.jpg"}],"research_interests":[{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology?f_ri=1032783","nofollow":true},{"id":18574,"name":"Inference","url":"https://www.academia.edu/Documents/in/Inference?f_ri=1032783","nofollow":true},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine?f_ri=1032783","nofollow":true},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783","nofollow":true},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"},{"id":2498386,"name":"Frontiers in Psychology","url":"https://www.academia.edu/Documents/in/Frontiers_in_Psychology?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_81226358" data-work_id="81226358" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/81226358/Bayesian_Inference_in_Semiparametric_Mixed_Models_for_Longitudinal_Data">Bayesian Inference in Semiparametric Mixed Models for Longitudinal Data</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We consider Bayesian inference in semiparametric mixed models (SPMMs) for longitudinal data. SPMMs are a class of models that use a nonparametric function to model a time effect, a parametric function to model other covariate effects, and... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_81226358" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We consider Bayesian inference in semiparametric mixed models (SPMMs) for longitudinal data. SPMMs are a class of models that use a nonparametric function to model a time effect, a parametric function to model other covariate effects, and parametric or nonparametric random effects to account for the within-subject correlation. We model the nonparametric function using a Bayesian formulation of a cubic smoothing spline, and the random effect distribution using a normal distribution and alternatively a nonparametric Dirichlet process (DP) prior. When the random effect distribution is assumed to be normal, we propose a uniform shrinkage prior (USP) for the variance components and the smoothing parameter. When the random effect distribution is modeled nonparametrically, we use a DP prior with a normal base measure and propose a USP for the hyperparameters of the DP base measure. We argue that the commonly assumed DP prior implies a non-zero mean of the random effect distribution, even when a base measure with mean zero is specified. This implies weak identifiability for the fixed effects, and can therefore lead to biased estimators and poor inference for the regression coefficients and the spline estimator of the nonparametric function. We propose an adjustment using a post-processing technique. We show that under mild conditions the posterior is proper under the proposed USP, a flat prior for the fixed effect parameters, and an improper prior for the residual variance. We illustrate the proposed approach using a longitudinal hormone dataset, and carry out extensive simulation studies to compare its finite sample performance with existing methods.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/81226358" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e34ffa942225828fcacdb3e89e03ea4f" rel="nofollow" data-download="{"attachment_id":87344959,"asset_id":81226358,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/87344959/download_file?st=MTc0MDUzMzUxMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="91658422" href="https://independent.academia.edu/PeterM%C3%BCller146">Peter Müller</a><script data-card-contents-for-user="91658422" type="text/json">{"id":91658422,"first_name":"Peter","last_name":"Müller","domain_name":"independent","page_name":"PeterMüller146","display_name":"Peter Müller","profile_url":"https://independent.academia.edu/PeterM%C3%BCller146?f_ri=1032783","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_81226358 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="81226358"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 81226358, container: ".js-paper-rank-work_81226358", }); 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$(".js-view-count[data-work-id=81226358]").text(description); $(".js-view-count-work_81226358").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_81226358").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="81226358"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">20</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="300" rel="nofollow" href="https://www.academia.edu/Documents/in/Mathematics">Mathematics</a>, <script data-card-contents-for-ri="300" type="text/json">{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="428" rel="nofollow" href="https://www.academia.edu/Documents/in/Algorithms">Algorithms</a>, <script data-card-contents-for-ri="428" type="text/json">{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9173" rel="nofollow" href="https://www.academia.edu/Documents/in/Biometrics">Biometrics</a>, <script data-card-contents-for-ri="9173" type="text/json">{"id":9173,"name":"Biometrics","url":"https://www.academia.edu/Documents/in/Biometrics?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="18574" rel="nofollow" href="https://www.academia.edu/Documents/in/Inference">Inference</a><script data-card-contents-for-ri="18574" type="text/json">{"id":18574,"name":"Inference","url":"https://www.academia.edu/Documents/in/Inference?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=81226358]'), work: {"id":81226358,"title":"Bayesian Inference in Semiparametric Mixed Models for Longitudinal Data","created_at":"2022-06-11T04:37:33.812-07:00","url":"https://www.academia.edu/81226358/Bayesian_Inference_in_Semiparametric_Mixed_Models_for_Longitudinal_Data?f_ri=1032783","dom_id":"work_81226358","summary":"We consider Bayesian inference in semiparametric mixed models (SPMMs) for longitudinal data. SPMMs are a class of models that use a nonparametric function to model a time effect, a parametric function to model other covariate effects, and parametric or nonparametric random effects to account for the within-subject correlation. We model the nonparametric function using a Bayesian formulation of a cubic smoothing spline, and the random effect distribution using a normal distribution and alternatively a nonparametric Dirichlet process (DP) prior. When the random effect distribution is assumed to be normal, we propose a uniform shrinkage prior (USP) for the variance components and the smoothing parameter. When the random effect distribution is modeled nonparametrically, we use a DP prior with a normal base measure and propose a USP for the hyperparameters of the DP base measure. We argue that the commonly assumed DP prior implies a non-zero mean of the random effect distribution, even when a base measure with mean zero is specified. This implies weak identifiability for the fixed effects, and can therefore lead to biased estimators and poor inference for the regression coefficients and the spline estimator of the nonparametric function. We propose an adjustment using a post-processing technique. We show that under mild conditions the posterior is proper under the proposed USP, a flat prior for the fixed effect parameters, and an improper prior for the residual variance. We illustrate the proposed approach using a longitudinal hormone dataset, and carry out extensive simulation studies to compare its finite sample performance with existing methods.","downloadable_attachments":[{"id":87344959,"asset_id":81226358,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":91658422,"first_name":"Peter","last_name":"Müller","domain_name":"independent","page_name":"PeterMüller146","display_name":"Peter Müller","profile_url":"https://independent.academia.edu/PeterM%C3%BCller146?f_ri=1032783","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=1032783","nofollow":true},{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=1032783","nofollow":true},{"id":9173,"name":"Biometrics","url":"https://www.academia.edu/Documents/in/Biometrics?f_ri=1032783","nofollow":true},{"id":18574,"name":"Inference","url":"https://www.academia.edu/Documents/in/Inference?f_ri=1032783","nofollow":true},{"id":19537,"name":"Biometry","url":"https://www.academia.edu/Documents/in/Biometry?f_ri=1032783"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine?f_ri=1032783"},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783"},{"id":69542,"name":"Computer Simulation","url":"https://www.academia.edu/Documents/in/Computer_Simulation?f_ri=1032783"},{"id":143542,"name":"Dirichlet process","url":"https://www.academia.edu/Documents/in/Dirichlet_process?f_ri=1032783"},{"id":255453,"name":"Information Storage and Retrieval","url":"https://www.academia.edu/Documents/in/Information_Storage_and_Retrieval?f_ri=1032783"},{"id":303912,"name":"Identifiability","url":"https://www.academia.edu/Documents/in/Identifiability?f_ri=1032783"},{"id":330953,"name":"Longitudinal Studies","url":"https://www.academia.edu/Documents/in/Longitudinal_Studies?f_ri=1032783"},{"id":439627,"name":"Longitudinal data","url":"https://www.academia.edu/Documents/in/Longitudinal_data?f_ri=1032783"},{"id":509785,"name":"Simulation Study","url":"https://www.academia.edu/Documents/in/Simulation_Study?f_ri=1032783"},{"id":880279,"name":"Bayes Theorem","url":"https://www.academia.edu/Documents/in/Bayes_Theorem-1?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"},{"id":1142720,"name":"Normal Distribution","url":"https://www.academia.edu/Documents/in/Normal_Distribution?f_ri=1032783"},{"id":1294522,"name":"Fixed Effects","url":"https://www.academia.edu/Documents/in/Fixed_Effects?f_ri=1032783"},{"id":4046488,"name":"Smoothing Spline","url":"https://www.academia.edu/Documents/in/Smoothing_Spline?f_ri=1032783"},{"id":4112474,"name":"Random effects","url":"https://www.academia.edu/Documents/in/Random_effects-1?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_80977594" data-work_id="80977594" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/80977594/Crossed_Linear_Gaussian_Bayesian_Networks_Parsimonious_Models">Crossed Linear Gaussian Bayesian Networks, Parsimonious Models</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Linear Gaussian Bayesian networks can dramatically reduce the parametric dimension of the covariance matrices in the framework of multivariate multiple regression models. This idea is developed using structured, crossed directed acyclic... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_80977594" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Linear Gaussian Bayesian networks can dramatically reduce the parametric dimension of the covariance matrices in the framework of multivariate multiple regression models. This idea is developed using structured, crossed directed acyclic graphs (DAGs) when node sets can be interpreted as the cartesian product of two sets. Some interesting properties of these DAGs are shown as well as the probability distributions of the associated Bayesian networks. A numerical experiment on simulated data was performed to check that the idea could be applied in practice. This modelling is applied to the prediction of body composition from easily measurable covariates and compared with the results of a saturated regression prediction. Résumé : Dans cet article, nous proposons de substituer aux régressions linéaires multivariées classiques des sous modélisations plus parcimonieuses construites à l'aide de réseaux bayésiens gaussiens. L'idée est d'améliorer la prédiction de variables par des covariables, grâce à une réduction sensible de la dimension paramétrique de la matrice de variance-covariance. Une mise en oeuvre est développée par l'utilisation de DAG (graphe orienté sans circuit) structurés lorsque l'ensemble des noeuds à modéliser est un produit cartésien de deux ensembles. Un certain nombre de propriétés intéressantes de ces DAG et des réseaux bayésiens associés en découle. Une expérimentation numérique basée sur des données simulées est réalisée pour vérifier la faisabilité de la proposition à partir de données lorsque la structure du DAG n'est pas connue. Enfin, la proposition est appliquée à la prédiction de la composition corporelle à partir de covariables faciles à obtenir. Les résultats obtenus par une recherche systématique de cette classe de réseaux bayésiens sont comparés avec la prédiction du modèle saturé de regression multiple multivariée.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/80977594" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="60a3cba91be6f0c192fd1b409b7a37b9" rel="nofollow" data-download="{"attachment_id":87180350,"asset_id":80977594,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/87180350/download_file?st=MTc0MDUzMzUxMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="50388490" href="https://independent.academia.edu/JeanBaptisteDenis">Jean-Baptiste Denis</a><script data-card-contents-for-user="50388490" type="text/json">{"id":50388490,"first_name":"Jean-Baptiste","last_name":"Denis","domain_name":"independent","page_name":"JeanBaptisteDenis","display_name":"Jean-Baptiste Denis","profile_url":"https://independent.academia.edu/JeanBaptisteDenis?f_ri=1032783","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_80977594 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="80977594"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 80977594, container: ".js-paper-rank-work_80977594", }); });</script></li><li class="js-percentile-work_80977594 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80977594; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_80977594"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_80977594 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="80977594"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80977594; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80977594]").text(description); $(".js-view-count-work_80977594").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_80977594").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="80977594"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="7968" rel="nofollow" href="https://www.academia.edu/Documents/in/Prediction">Prediction</a>, <script data-card-contents-for-ri="7968" type="text/json">{"id":7968,"name":"Prediction","url":"https://www.academia.edu/Documents/in/Prediction?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="51529" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Inference">Bayesian Inference</a>, <script data-card-contents-for-ri="51529" type="text/json">{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="274599" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Network">Bayesian Network</a><script data-card-contents-for-ri="274599" type="text/json">{"id":274599,"name":"Bayesian Network","url":"https://www.academia.edu/Documents/in/Bayesian_Network?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=80977594]'), work: {"id":80977594,"title":"Crossed Linear Gaussian Bayesian Networks, Parsimonious Models","created_at":"2022-06-07T23:12:38.825-07:00","url":"https://www.academia.edu/80977594/Crossed_Linear_Gaussian_Bayesian_Networks_Parsimonious_Models?f_ri=1032783","dom_id":"work_80977594","summary":"Linear Gaussian Bayesian networks can dramatically reduce the parametric dimension of the covariance matrices in the framework of multivariate multiple regression models. This idea is developed using structured, crossed directed acyclic graphs (DAGs) when node sets can be interpreted as the cartesian product of two sets. Some interesting properties of these DAGs are shown as well as the probability distributions of the associated Bayesian networks. A numerical experiment on simulated data was performed to check that the idea could be applied in practice. This modelling is applied to the prediction of body composition from easily measurable covariates and compared with the results of a saturated regression prediction. Résumé : Dans cet article, nous proposons de substituer aux régressions linéaires multivariées classiques des sous modélisations plus parcimonieuses construites à l'aide de réseaux bayésiens gaussiens. L'idée est d'améliorer la prédiction de variables par des covariables, grâce à une réduction sensible de la dimension paramétrique de la matrice de variance-covariance. Une mise en oeuvre est développée par l'utilisation de DAG (graphe orienté sans circuit) structurés lorsque l'ensemble des noeuds à modéliser est un produit cartésien de deux ensembles. Un certain nombre de propriétés intéressantes de ces DAG et des réseaux bayésiens associés en découle. Une expérimentation numérique basée sur des données simulées est réalisée pour vérifier la faisabilité de la proposition à partir de données lorsque la structure du DAG n'est pas connue. Enfin, la proposition est appliquée à la prédiction de la composition corporelle à partir de covariables faciles à obtenir. Les résultats obtenus par une recherche systématique de cette classe de réseaux bayésiens sont comparés avec la prédiction du modèle saturé de regression multiple multivariée.","downloadable_attachments":[{"id":87180350,"asset_id":80977594,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":50388490,"first_name":"Jean-Baptiste","last_name":"Denis","domain_name":"independent","page_name":"JeanBaptisteDenis","display_name":"Jean-Baptiste Denis","profile_url":"https://independent.academia.edu/JeanBaptisteDenis?f_ri=1032783","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true},{"id":7968,"name":"Prediction","url":"https://www.academia.edu/Documents/in/Prediction?f_ri=1032783","nofollow":true},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783","nofollow":true},{"id":274599,"name":"Bayesian Network","url":"https://www.academia.edu/Documents/in/Bayesian_Network?f_ri=1032783","nofollow":true},{"id":342314,"name":"Gaussian","url":"https://www.academia.edu/Documents/in/Gaussian?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"},{"id":4066813,"name":"multivariate multiple regression","url":"https://www.academia.edu/Documents/in/multivariate_multiple_regression?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_80702706" data-work_id="80702706" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/80702706/Bayesian_QTL_mapping_using_skewed_Student_t_distributions">Bayesian QTL mapping using skewed Student-t distributions</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In most QTL mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may lead to detection of false positive QTL. To improve the robustness of Bayesian QTL mapping methods, the normal... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_80702706" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In most QTL mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may lead to detection of false positive QTL. To improve the robustness of Bayesian QTL mapping methods, the normal distribution for residuals is replaced with a skewed Student-t distribution. The latter distribution is able to account for both heavy tails and skewness, and both components are each controlled by a single parameter. The Bayesian QTL mapping method using a skewed Student-t distribution is evaluated with simulated data sets under five different scenarios of residual error distributions and QTL effects.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/80702706" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="01f90ee17e040a1879883be7f203d1a3" rel="nofollow" data-download="{"attachment_id":86997530,"asset_id":80702706,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/86997530/download_file?st=MTc0MDUzMzUxMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="67391898" href="https://independent.academia.edu/InaHoeschele">Ina Hoeschele</a><script data-card-contents-for-user="67391898" type="text/json">{"id":67391898,"first_name":"Ina","last_name":"Hoeschele","domain_name":"independent","page_name":"InaHoeschele","display_name":"Ina Hoeschele","profile_url":"https://independent.academia.edu/InaHoeschele?f_ri=1032783","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_80702706 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="80702706"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 80702706, container: ".js-paper-rank-work_80702706", }); });</script></li><li class="js-percentile-work_80702706 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80702706; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_80702706"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_80702706 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="80702706"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80702706; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80702706]").text(description); $(".js-view-count-work_80702706").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_80702706").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="80702706"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">19</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="428" rel="nofollow" href="https://www.academia.edu/Documents/in/Algorithms">Algorithms</a>, <script data-card-contents-for-ri="428" type="text/json">{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="3216" rel="nofollow" href="https://www.academia.edu/Documents/in/Genomics">Genomics</a>, <script data-card-contents-for-ri="3216" type="text/json">{"id":3216,"name":"Genomics","url":"https://www.academia.edu/Documents/in/Genomics?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5590" rel="nofollow" href="https://www.academia.edu/Documents/in/Biomathematics">Biomathematics</a>, <script data-card-contents-for-ri="5590" type="text/json">{"id":5590,"name":"Biomathematics","url":"https://www.academia.edu/Documents/in/Biomathematics?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="7710" rel="nofollow" href="https://www.academia.edu/Documents/in/Biology">Biology</a><script data-card-contents-for-ri="7710" type="text/json">{"id":7710,"name":"Biology","url":"https://www.academia.edu/Documents/in/Biology?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=80702706]'), work: {"id":80702706,"title":"Bayesian QTL mapping using skewed Student-t distributions","created_at":"2022-06-04T15:32:34.314-07:00","url":"https://www.academia.edu/80702706/Bayesian_QTL_mapping_using_skewed_Student_t_distributions?f_ri=1032783","dom_id":"work_80702706","summary":"In most QTL mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may lead to detection of false positive QTL. To improve the robustness of Bayesian QTL mapping methods, the normal distribution for residuals is replaced with a skewed Student-t distribution. The latter distribution is able to account for both heavy tails and skewness, and both components are each controlled by a single parameter. The Bayesian QTL mapping method using a skewed Student-t distribution is evaluated with simulated data sets under five different scenarios of residual error distributions and QTL effects.","downloadable_attachments":[{"id":86997530,"asset_id":80702706,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":67391898,"first_name":"Ina","last_name":"Hoeschele","domain_name":"independent","page_name":"InaHoeschele","display_name":"Ina Hoeschele","profile_url":"https://independent.academia.edu/InaHoeschele?f_ri=1032783","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=1032783","nofollow":true},{"id":3216,"name":"Genomics","url":"https://www.academia.edu/Documents/in/Genomics?f_ri=1032783","nofollow":true},{"id":5590,"name":"Biomathematics","url":"https://www.academia.edu/Documents/in/Biomathematics?f_ri=1032783","nofollow":true},{"id":7710,"name":"Biology","url":"https://www.academia.edu/Documents/in/Biology?f_ri=1032783","nofollow":true},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine?f_ri=1032783"},{"id":47884,"name":"Biological Sciences","url":"https://www.academia.edu/Documents/in/Biological_Sciences?f_ri=1032783"},{"id":67962,"name":"Markov chains","url":"https://www.academia.edu/Documents/in/Markov_chains?f_ri=1032783"},{"id":69542,"name":"Computer Simulation","url":"https://www.academia.edu/Documents/in/Computer_Simulation?f_ri=1032783"},{"id":372410,"name":"Genotype","url":"https://www.academia.edu/Documents/in/Genotype?f_ri=1032783"},{"id":413806,"name":"Inbreeding","url":"https://www.academia.edu/Documents/in/Inbreeding?f_ri=1032783"},{"id":584601,"name":"Chi Square Distribution","url":"https://www.academia.edu/Documents/in/Chi_Square_Distribution?f_ri=1032783"},{"id":595993,"name":"Markov chain","url":"https://www.academia.edu/Documents/in/Markov_chain?f_ri=1032783"},{"id":812412,"name":"Genetic Mapping","url":"https://www.academia.edu/Documents/in/Genetic_Mapping?f_ri=1032783"},{"id":880279,"name":"Bayes Theorem","url":"https://www.academia.edu/Documents/in/Bayes_Theorem-1?f_ri=1032783"},{"id":893194,"name":"Inverse","url":"https://www.academia.edu/Documents/in/Inverse?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"},{"id":1333436,"name":"Monte Carlo Method","url":"https://www.academia.edu/Documents/in/Monte_Carlo_Method?f_ri=1032783"},{"id":2468494,"name":"False Positive","url":"https://www.academia.edu/Documents/in/False_Positive?f_ri=1032783"},{"id":2780137,"name":"alleles","url":"https://www.academia.edu/Documents/in/alleles?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_80087811" data-work_id="80087811" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/80087811/Electrical_Capacitance_Tomography_Using_Bayesian_Inference">Electrical Capacitance Tomography Using Bayesian Inference</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Electrical capacitance tomography is a non-invasive imaging technique that uses measured trans-capacitances outside a medium to recover the unknown permittivity in the medium. We use Bayesian inference to solve the problem of recovering... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_80087811" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Electrical capacitance tomography is a non-invasive imaging technique that uses measured trans-capacitances outside a medium to recover the unknown permittivity in the medium. We use Bayesian inference to solve the problem of recovering the unknown shape of a constant permittivity inclusion in an otherwise uniform background. Calculated statistics give accurate estimates of inclusion area, and other properties, when using measured data.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/80087811" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="de55abda7e19c30fa53e256254e63c3a" rel="nofollow" data-download="{"attachment_id":86586290,"asset_id":80087811,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/86586290/download_file?st=MTc0MDUzMzUxMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="37571129" href="https://tugraz.academia.edu/DanielWatzenig">Daniel Watzenig</a><script data-card-contents-for-user="37571129" type="text/json">{"id":37571129,"first_name":"Daniel","last_name":"Watzenig","domain_name":"tugraz","page_name":"DanielWatzenig","display_name":"Daniel Watzenig","profile_url":"https://tugraz.academia.edu/DanielWatzenig?f_ri=1032783","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_80087811 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="80087811"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 80087811, container: ".js-paper-rank-work_80087811", }); 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$(".js-view-count[data-work-id=80087811]").text(description); $(".js-view-count-work_80087811").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_80087811").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="80087811"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="51529" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Inference">Bayesian Inference</a>, <script data-card-contents-for-ri="51529" type="text/json">{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="293769" rel="nofollow" href="https://www.academia.edu/Documents/in/Electrical_Capacitance_Tomography">Electrical Capacitance Tomography</a>, <script data-card-contents-for-ri="293769" type="text/json">{"id":293769,"name":"Electrical Capacitance Tomography","url":"https://www.academia.edu/Documents/in/Electrical_Capacitance_Tomography?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1032783" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Probability">Bayesian Probability</a><script data-card-contents-for-ri="1032783" type="text/json">{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=80087811]'), work: {"id":80087811,"title":"Electrical Capacitance Tomography Using Bayesian Inference","created_at":"2022-05-27T21:52:51.752-07:00","url":"https://www.academia.edu/80087811/Electrical_Capacitance_Tomography_Using_Bayesian_Inference?f_ri=1032783","dom_id":"work_80087811","summary":"Electrical capacitance tomography is a non-invasive imaging technique that uses measured trans-capacitances outside a medium to recover the unknown permittivity in the medium. We use Bayesian inference to solve the problem of recovering the unknown shape of a constant permittivity inclusion in an otherwise uniform background. Calculated statistics give accurate estimates of inclusion area, and other properties, when using measured data.","downloadable_attachments":[{"id":86586290,"asset_id":80087811,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":37571129,"first_name":"Daniel","last_name":"Watzenig","domain_name":"tugraz","page_name":"DanielWatzenig","display_name":"Daniel Watzenig","profile_url":"https://tugraz.academia.edu/DanielWatzenig?f_ri=1032783","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783","nofollow":true},{"id":293769,"name":"Electrical Capacitance Tomography","url":"https://www.academia.edu/Documents/in/Electrical_Capacitance_Tomography?f_ri=1032783","nofollow":true},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_79645024" data-work_id="79645024" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/79645024/A_User_Guided_Bayesian_Framework_for_Ensemble_Feature_Selection_in_Life_Science_Applications_UBayFS_">A User-Guided Bayesian Framework for Ensemble Feature Selection in Life Science Applications (UBayFS)</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Training predictive models on high-dimensional datasets is a challenging task in artificial intelligence. Users must take measures to prevent overfitting and keep model complexity low. Thus, the feature selection plays a key role in data... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_79645024" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Training predictive models on high-dimensional datasets is a challenging task in artificial intelligence. Users must take measures to prevent overfitting and keep model complexity low. Thus, the feature selection plays a key role in data preprocessing and delivers insights into the systematic variation in the data. The latter aspect is crucial in domains that rely on model interpretability, such as life sciences. We propose UBayFS, an ensemble feature selection technique, embedded in a Bayesian statistical framework. Our approach enhances the feature selection process by considering two sources of information: data and domain knowledge. Therefore, we build an ensemble of elementary feature selectors that extract information from empirical data, leading to a meta-model, which compensates for inconsistencies between elementary feature selectors. The user guides UBayFS by weighting features and penalizing specific feature combinations. The framework builds on a multinomial likelihood a...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/79645024" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="63c6f06db586363cccb2c6af2a65ce57" rel="nofollow" data-download="{"attachment_id":86289228,"asset_id":79645024,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/86289228/download_file?st=MTc0MDUzMzUxMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="29425978" href="https://uni-klu.academia.edu/JPilz">Jürgen Pilz</a><script data-card-contents-for-user="29425978" type="text/json">{"id":29425978,"first_name":"Jürgen","last_name":"Pilz","domain_name":"uni-klu","page_name":"JPilz","display_name":"Jürgen Pilz","profile_url":"https://uni-klu.academia.edu/JPilz?f_ri=1032783","photo":"https://0.academia-photos.com/29425978/21201359/20650696/s65_j_rgen.pilz.jpg"}</script></span></span></li><li class="js-paper-rank-work_79645024 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="79645024"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 79645024, container: ".js-paper-rank-work_79645024", }); });</script></li><li class="js-percentile-work_79645024 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79645024; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_79645024"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_79645024 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="79645024"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79645024; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79645024]").text(description); $(".js-view-count-work_79645024").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_79645024").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="79645024"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="300" rel="nofollow" href="https://www.academia.edu/Documents/in/Mathematics">Mathematics</a>, <script data-card-contents-for-ri="300" type="text/json">{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="43619" rel="nofollow" href="https://www.academia.edu/Documents/in/Feature_Selection">Feature Selection</a><script data-card-contents-for-ri="43619" type="text/json">{"id":43619,"name":"Feature Selection","url":"https://www.academia.edu/Documents/in/Feature_Selection?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=79645024]'), work: {"id":79645024,"title":"A User-Guided Bayesian Framework for Ensemble Feature Selection in Life Science Applications (UBayFS)","created_at":"2022-05-22T05:15:45.256-07:00","url":"https://www.academia.edu/79645024/A_User_Guided_Bayesian_Framework_for_Ensemble_Feature_Selection_in_Life_Science_Applications_UBayFS_?f_ri=1032783","dom_id":"work_79645024","summary":"Training predictive models on high-dimensional datasets is a challenging task in artificial intelligence. Users must take measures to prevent overfitting and keep model complexity low. Thus, the feature selection plays a key role in data preprocessing and delivers insights into the systematic variation in the data. The latter aspect is crucial in domains that rely on model interpretability, such as life sciences. We propose UBayFS, an ensemble feature selection technique, embedded in a Bayesian statistical framework. Our approach enhances the feature selection process by considering two sources of information: data and domain knowledge. Therefore, we build an ensemble of elementary feature selectors that extract information from empirical data, leading to a meta-model, which compensates for inconsistencies between elementary feature selectors. The user guides UBayFS by weighting features and penalizing specific feature combinations. The framework builds on a multinomial likelihood a...","downloadable_attachments":[{"id":86289228,"asset_id":79645024,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":29425978,"first_name":"Jürgen","last_name":"Pilz","domain_name":"uni-klu","page_name":"JPilz","display_name":"Jürgen Pilz","profile_url":"https://uni-klu.academia.edu/JPilz?f_ri=1032783","photo":"https://0.academia-photos.com/29425978/21201359/20650696/s65_j_rgen.pilz.jpg"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=1032783","nofollow":true},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1032783","nofollow":true},{"id":43619,"name":"Feature Selection","url":"https://www.academia.edu/Documents/in/Feature_Selection?f_ri=1032783","nofollow":true},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"},{"id":3193313,"name":"arXiv","url":"https://www.academia.edu/Documents/in/arXiv?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_77479630" data-work_id="77479630" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/77479630/Bayesian_Inference_Using_Gibbs_Sampling_in_Applications_and_Curricula_of_Decision_Analysis">Bayesian Inference Using Gibbs Sampling in Applications and Curricula of Decision Analysis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Applications and curricula of decision analysis currently do not include methods to compute Bayes' rule and obtain posteriors for non-conjugate prior distributions. The current convention is to force the decision maker's belief to take... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_77479630" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Applications and curricula of decision analysis currently do not include methods to compute Bayes' rule and obtain posteriors for non-conjugate prior distributions. The current convention is to force the decision maker's belief to take the form of a conjugate distribution, leading to a suboptimal decision. BUGS software, which uses MCMC methods, is numerically capable of obtaining posteriors for non-conjugate priors. By using the decision maker's true non-conjugate belief, the problems explored suggest that BUGS is able to produce a posterior distribution which leads to optimal decision making. Other methods exist which can use nonconjugate priors, but they must be implemented ad hoc as they do not have any supporting software. BUGS offers the distinct advantage of being implemented in existing software. This software has a gradual learning curve, and with simple coding can solve a wide range of decision analysis problems. BUGS is useful in making optimal decisions, and it is reasonably easy to learn and implement, therefore there is value in including BUGS in decision analysis curricula.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/77479630" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="13c8526a7381517ffa922540547c496a" rel="nofollow" data-download="{"attachment_id":84828761,"asset_id":77479630,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/84828761/download_file?st=MTc0MDUzMzUxMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="56540691" href="https://independent.academia.edu/danielfrances1">daniel frances</a><script data-card-contents-for-user="56540691" type="text/json">{"id":56540691,"first_name":"daniel","last_name":"frances","domain_name":"independent","page_name":"danielfrances1","display_name":"daniel frances","profile_url":"https://independent.academia.edu/danielfrances1?f_ri=1032783","photo":"https://0.academia-photos.com/56540691/60227745/48485250/s65_daniel.frances.png"}</script></span></span></li><li class="js-paper-rank-work_77479630 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="77479630"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 77479630, container: ".js-paper-rank-work_77479630", }); 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$(".js-view-count[data-work-id=77479630]").text(description); $(".js-view-count-work_77479630").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_77479630").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="77479630"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="724" rel="nofollow" href="https://www.academia.edu/Documents/in/Economics">Economics</a>, <script data-card-contents-for-ri="724" type="text/json">{"id":724,"name":"Economics","url":"https://www.academia.edu/Documents/in/Economics?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="13424" rel="nofollow" href="https://www.academia.edu/Documents/in/Decision_Analysis">Decision Analysis</a>, <script data-card-contents-for-ri="13424" type="text/json">{"id":13424,"name":"Decision Analysis","url":"https://www.academia.edu/Documents/in/Decision_Analysis?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="51529" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Inference">Bayesian Inference</a><script data-card-contents-for-ri="51529" type="text/json">{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=77479630]'), work: {"id":77479630,"title":"Bayesian Inference Using Gibbs Sampling in Applications and Curricula of Decision Analysis","created_at":"2022-04-24T12:15:48.321-07:00","url":"https://www.academia.edu/77479630/Bayesian_Inference_Using_Gibbs_Sampling_in_Applications_and_Curricula_of_Decision_Analysis?f_ri=1032783","dom_id":"work_77479630","summary":"Applications and curricula of decision analysis currently do not include methods to compute Bayes' rule and obtain posteriors for non-conjugate prior distributions. The current convention is to force the decision maker's belief to take the form of a conjugate distribution, leading to a suboptimal decision. BUGS software, which uses MCMC methods, is numerically capable of obtaining posteriors for non-conjugate priors. By using the decision maker's true non-conjugate belief, the problems explored suggest that BUGS is able to produce a posterior distribution which leads to optimal decision making. Other methods exist which can use nonconjugate priors, but they must be implemented ad hoc as they do not have any supporting software. BUGS offers the distinct advantage of being implemented in existing software. This software has a gradual learning curve, and with simple coding can solve a wide range of decision analysis problems. BUGS is useful in making optimal decisions, and it is reasonably easy to learn and implement, therefore there is value in including BUGS in decision analysis curricula.","downloadable_attachments":[{"id":84828761,"asset_id":77479630,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":56540691,"first_name":"daniel","last_name":"frances","domain_name":"independent","page_name":"danielfrances1","display_name":"daniel frances","profile_url":"https://independent.academia.edu/danielfrances1?f_ri=1032783","photo":"https://0.academia-photos.com/56540691/60227745/48485250/s65_daniel.frances.png"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true},{"id":724,"name":"Economics","url":"https://www.academia.edu/Documents/in/Economics?f_ri=1032783","nofollow":true},{"id":13424,"name":"Decision Analysis","url":"https://www.academia.edu/Documents/in/Decision_Analysis?f_ri=1032783","nofollow":true},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783","nofollow":true},{"id":229603,"name":"Gibbs sampling","url":"https://www.academia.edu/Documents/in/Gibbs_sampling?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_77180622" data-work_id="77180622" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/77180622/Bayesian_Estimation_of_NOEM_Models_Identification_and_Inference_in_Small_Samples">Bayesian Estimation of NOEM Models: Identification and Inference in Small Samples</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The global slack hypothesis (e.g., ) is central to the discussion of the trade-offs monetary policy faces in an increasingly more open world economy. Open-Economy (forward-looking) New Keynesian Phillips curves describe how expected... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_77180622" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The global slack hypothesis (e.g., ) is central to the discussion of the trade-offs monetary policy faces in an increasingly more open world economy. Open-Economy (forward-looking) New Keynesian Phillips curves describe how expected future inflation and a measure of global output gap (global slack) affect the current inflation rate. This paper studies the (potential) weak identification of these relationships in the context of a fully-specified structural model using Bayesian estimation techniques. We trace the problems to sample size, rather than misspecification bias. We conclude that standard macroeconomic time series with a coverage of less than forty years are subject to potentially serious identification issues, and also to model selection errors. We recommend estimation with simulated data prior to bringing the model to the actual data as a way of detecting parameters that are susceptible to weak identification in short samples.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/77180622" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="6deab1d60beecd8f6782b727c8f90501" rel="nofollow" data-download="{"attachment_id":84634742,"asset_id":77180622,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/84634742/download_file?st=MTc0MDUzMzUxMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="36363341" href="https://dallasfed.academia.edu/Enrique_MartinezGarcia">Enrique Martínez García</a><script data-card-contents-for-user="36363341" type="text/json">{"id":36363341,"first_name":"Enrique","last_name":"Martínez García","domain_name":"dallasfed","page_name":"Enrique_MartinezGarcia","display_name":"Enrique Martínez García","profile_url":"https://dallasfed.academia.edu/Enrique_MartinezGarcia?f_ri=1032783","photo":"https://0.academia-photos.com/36363341/11565130/12897985/s65_enrique.mart_nez-garc_a.jpg"}</script></span></span></li><li class="js-paper-rank-work_77180622 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="77180622"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 77180622, container: ".js-paper-rank-work_77180622", }); });</script></li><li class="js-percentile-work_77180622 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77180622; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_77180622"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_77180622 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="77180622"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77180622; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77180622]").text(description); $(".js-view-count-work_77180622").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_77180622").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="77180622"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">17</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="747" rel="nofollow" href="https://www.academia.edu/Documents/in/Econometrics">Econometrics</a>, <script data-card-contents-for-ri="747" type="text/json">{"id":747,"name":"Econometrics","url":"https://www.academia.edu/Documents/in/Econometrics?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4456" rel="nofollow" href="https://www.academia.edu/Documents/in/Time_Series">Time Series</a>, <script data-card-contents-for-ri="4456" type="text/json">{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="18574" rel="nofollow" href="https://www.academia.edu/Documents/in/Inference">Inference</a><script data-card-contents-for-ri="18574" type="text/json">{"id":18574,"name":"Inference","url":"https://www.academia.edu/Documents/in/Inference?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=77180622]'), work: {"id":77180622,"title":"Bayesian Estimation of NOEM Models: Identification and Inference in Small Samples","created_at":"2022-04-21T06:21:30.073-07:00","url":"https://www.academia.edu/77180622/Bayesian_Estimation_of_NOEM_Models_Identification_and_Inference_in_Small_Samples?f_ri=1032783","dom_id":"work_77180622","summary":"The global slack hypothesis (e.g., ) is central to the discussion of the trade-offs monetary policy faces in an increasingly more open world economy. Open-Economy (forward-looking) New Keynesian Phillips curves describe how expected future inflation and a measure of global output gap (global slack) affect the current inflation rate. This paper studies the (potential) weak identification of these relationships in the context of a fully-specified structural model using Bayesian estimation techniques. We trace the problems to sample size, rather than misspecification bias. We conclude that standard macroeconomic time series with a coverage of less than forty years are subject to potentially serious identification issues, and also to model selection errors. We recommend estimation with simulated data prior to bringing the model to the actual data as a way of detecting parameters that are susceptible to weak identification in short samples.","downloadable_attachments":[{"id":84634742,"asset_id":77180622,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":36363341,"first_name":"Enrique","last_name":"Martínez García","domain_name":"dallasfed","page_name":"Enrique_MartinezGarcia","display_name":"Enrique Martínez García","profile_url":"https://dallasfed.academia.edu/Enrique_MartinezGarcia?f_ri=1032783","photo":"https://0.academia-photos.com/36363341/11565130/12897985/s65_enrique.mart_nez-garc_a.jpg"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true},{"id":747,"name":"Econometrics","url":"https://www.academia.edu/Documents/in/Econometrics?f_ri=1032783","nofollow":true},{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=1032783","nofollow":true},{"id":18574,"name":"Inference","url":"https://www.academia.edu/Documents/in/Inference?f_ri=1032783","nofollow":true},{"id":34109,"name":"Bayesian estimation","url":"https://www.academia.edu/Documents/in/Bayesian_estimation?f_ri=1032783"},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783"},{"id":59054,"name":"Model Identification","url":"https://www.academia.edu/Documents/in/Model_Identification?f_ri=1032783"},{"id":63353,"name":"Identification","url":"https://www.academia.edu/Documents/in/Identification?f_ri=1032783"},{"id":69856,"name":"Social Science Research Network","url":"https://www.academia.edu/Documents/in/Social_Science_Research_Network?f_ri=1032783"},{"id":85344,"name":"Model Selection","url":"https://www.academia.edu/Documents/in/Model_Selection?f_ri=1032783"},{"id":196189,"name":"Sample Size","url":"https://www.academia.edu/Documents/in/Sample_Size?f_ri=1032783"},{"id":219924,"name":"Open Economy Macroeconomics","url":"https://www.academia.edu/Documents/in/Open_Economy_Macroeconomics?f_ri=1032783"},{"id":805001,"name":"Small samples","url":"https://www.academia.edu/Documents/in/Small_samples?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"},{"id":1643503,"name":"Structural model","url":"https://www.academia.edu/Documents/in/Structural_model?f_ri=1032783"},{"id":1951089,"name":"Bayesian Estimator","url":"https://www.academia.edu/Documents/in/Bayesian_Estimator?f_ri=1032783"},{"id":3021408,"name":"Econometric Model","url":"https://www.academia.edu/Documents/in/Econometric_Model?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_75268500" data-work_id="75268500" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/75268500/ANOVA_based_transformed_probabilistic_collocation_method_for_Bayesian_data_worth_analysis">ANOVA-based transformed probabilistic collocation method for Bayesian data-worth analysis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Abstract Bayesian theory provides a coherent framework in quantifying the data worth of measurements and estimating unknown parameters. Nevertheless, one common problem in Bayesian methods is the considerably high computational cost since... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_75268500" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Abstract Bayesian theory provides a coherent framework in quantifying the data worth of measurements and estimating unknown parameters. Nevertheless, one common problem in Bayesian methods is the considerably high computational cost since a large number of model evaluations is required in the likelihood evaluation. To address this issue, a new surrogate modeling method, i.e., ANOVA (analysis of variance)-based transformed probabilistic collocation method (ATPCM), is developed in this work. To cope with the strong nonlinearity, the model responses are transformed to the arrival times, which are then approximated with a set of low-order ANOVA components. The validity of the proposed method is demonstrated by synthetic numerical cases involving water and heat transport in the vadose zone. It is shown that, the ATPCM is more efficient than the existing surrogate modeling methods (e.g., PCM, ANOVA-based PCM and TPCM). At a very low computational cost, the ATPCM-based Bayesian data-worth analysis provides a quantitative metric in comparing different monitoring plans, and helps to improve the parameter estimation. Although the flow and heat transport in vadose zone is considered in this work, the proposed method can be equally applied in any other hydrologic problems.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/75268500" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="43886964" href="https://independent.academia.edu/QinzhuoLiao">Qinzhuo Liao</a><script data-card-contents-for-user="43886964" type="text/json">{"id":43886964,"first_name":"Qinzhuo","last_name":"Liao","domain_name":"independent","page_name":"QinzhuoLiao","display_name":"Qinzhuo Liao","profile_url":"https://independent.academia.edu/QinzhuoLiao?f_ri=1032783","photo":"https://0.academia-photos.com/43886964/13557293/14727882/s65_qinzhuo.liao.jpg"}</script></span></span></li><li class="js-paper-rank-work_75268500 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="75268500"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 75268500, container: ".js-paper-rank-work_75268500", }); });</script></li><li class="js-percentile-work_75268500 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75268500; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_75268500"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_75268500 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="75268500"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75268500; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75268500]").text(description); $(".js-view-count-work_75268500").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_75268500").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="75268500"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="55" rel="nofollow" href="https://www.academia.edu/Documents/in/Environmental_Engineering">Environmental Engineering</a>, <script data-card-contents-for-ri="55" type="text/json">{"id":55,"name":"Environmental Engineering","url":"https://www.academia.edu/Documents/in/Environmental_Engineering?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="73" rel="nofollow" href="https://www.academia.edu/Documents/in/Civil_Engineering">Civil Engineering</a>, <script data-card-contents-for-ri="73" type="text/json">{"id":73,"name":"Civil Engineering","url":"https://www.academia.edu/Documents/in/Civil_Engineering?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="300" rel="nofollow" href="https://www.academia.edu/Documents/in/Mathematics">Mathematics</a>, <script data-card-contents-for-ri="300" type="text/json">{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4526" rel="nofollow" href="https://www.academia.edu/Documents/in/Water_resources">Water resources</a><script data-card-contents-for-ri="4526" type="text/json">{"id":4526,"name":"Water resources","url":"https://www.academia.edu/Documents/in/Water_resources?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=75268500]'), work: {"id":75268500,"title":"ANOVA-based transformed probabilistic collocation method for Bayesian data-worth analysis","created_at":"2022-04-02T18:23:01.226-07:00","url":"https://www.academia.edu/75268500/ANOVA_based_transformed_probabilistic_collocation_method_for_Bayesian_data_worth_analysis?f_ri=1032783","dom_id":"work_75268500","summary":"Abstract Bayesian theory provides a coherent framework in quantifying the data worth of measurements and estimating unknown parameters. Nevertheless, one common problem in Bayesian methods is the considerably high computational cost since a large number of model evaluations is required in the likelihood evaluation. To address this issue, a new surrogate modeling method, i.e., ANOVA (analysis of variance)-based transformed probabilistic collocation method (ATPCM), is developed in this work. To cope with the strong nonlinearity, the model responses are transformed to the arrival times, which are then approximated with a set of low-order ANOVA components. The validity of the proposed method is demonstrated by synthetic numerical cases involving water and heat transport in the vadose zone. It is shown that, the ATPCM is more efficient than the existing surrogate modeling methods (e.g., PCM, ANOVA-based PCM and TPCM). At a very low computational cost, the ATPCM-based Bayesian data-worth analysis provides a quantitative metric in comparing different monitoring plans, and helps to improve the parameter estimation. Although the flow and heat transport in vadose zone is considered in this work, the proposed method can be equally applied in any other hydrologic problems.","downloadable_attachments":[],"ordered_authors":[{"id":43886964,"first_name":"Qinzhuo","last_name":"Liao","domain_name":"independent","page_name":"QinzhuoLiao","display_name":"Qinzhuo Liao","profile_url":"https://independent.academia.edu/QinzhuoLiao?f_ri=1032783","photo":"https://0.academia-photos.com/43886964/13557293/14727882/s65_qinzhuo.liao.jpg"}],"research_interests":[{"id":55,"name":"Environmental Engineering","url":"https://www.academia.edu/Documents/in/Environmental_Engineering?f_ri=1032783","nofollow":true},{"id":73,"name":"Civil Engineering","url":"https://www.academia.edu/Documents/in/Civil_Engineering?f_ri=1032783","nofollow":true},{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=1032783","nofollow":true},{"id":4526,"name":"Water resources","url":"https://www.academia.edu/Documents/in/Water_resources?f_ri=1032783","nofollow":true},{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_75248352" data-work_id="75248352" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/75248352/Bayesian_Inference_in_Spatial_Sample_Selection_Models">Bayesian Inference in Spatial Sample Selection Models</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this study, we consider Bayesian methods for the estimation of a sample selection model with spatially correlated disturbance terms. We design a set of Markov chain Monte Carlo (MCMC) algorithms based on the method of data... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_75248352" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this study, we consider Bayesian methods for the estimation of a sample selection model with spatially correlated disturbance terms. We design a set of Markov chain Monte Carlo (MCMC) algorithms based on the method of data augmentation. The natural parameterization for the covariance structure of our model involves an unidentified parameter that complicates posterior analysis. The unidentified parameter-the variance of the disturbance term in the selection equation-is handled in different ways in these algorithms to achieve identification for other parameters. The Bayesian estimator based on these algorithms can account for the selection bias and the full covariance structure implied by the spatial correlation. We illustrate the implementation of these algorithms through a simulation study.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/75248352" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="8f5010e8be8ab9b0f252500aa1bc487e" rel="nofollow" data-download="{"attachment_id":83093697,"asset_id":75248352,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/83093697/download_file?st=MTc0MDUzMzUxMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="105887119" href="https://independent.academia.edu/OsmanBurakDo%C4%9Fan">Osman Burak Doğan</a><script data-card-contents-for-user="105887119" type="text/json">{"id":105887119,"first_name":"Osman Burak","last_name":"Doğan","domain_name":"independent","page_name":"OsmanBurakDoğan","display_name":"Osman Burak Doğan","profile_url":"https://independent.academia.edu/OsmanBurakDo%C4%9Fan?f_ri=1032783","photo":"https://0.academia-photos.com/105887119/28222227/26425603/s65_osman_burak.do_an.jpg"}</script></span></span></li><li class="js-paper-rank-work_75248352 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="75248352"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 75248352, container: ".js-paper-rank-work_75248352", }); 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$(".js-view-count[data-work-id=75248352]").text(description); $(".js-view-count-work_75248352").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_75248352").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="75248352"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="724" rel="nofollow" href="https://www.academia.edu/Documents/in/Economics">Economics</a>, <script data-card-contents-for-ri="724" type="text/json">{"id":724,"name":"Economics","url":"https://www.academia.edu/Documents/in/Economics?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="51529" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Inference">Bayesian Inference</a>, <script data-card-contents-for-ri="51529" type="text/json">{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1032783" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Probability">Bayesian Probability</a><script data-card-contents-for-ri="1032783" type="text/json">{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=75248352]'), work: {"id":75248352,"title":"Bayesian Inference in Spatial Sample Selection Models","created_at":"2022-04-02T11:03:06.534-07:00","url":"https://www.academia.edu/75248352/Bayesian_Inference_in_Spatial_Sample_Selection_Models?f_ri=1032783","dom_id":"work_75248352","summary":"In this study, we consider Bayesian methods for the estimation of a sample selection model with spatially correlated disturbance terms. We design a set of Markov chain Monte Carlo (MCMC) algorithms based on the method of data augmentation. The natural parameterization for the covariance structure of our model involves an unidentified parameter that complicates posterior analysis. The unidentified parameter-the variance of the disturbance term in the selection equation-is handled in different ways in these algorithms to achieve identification for other parameters. The Bayesian estimator based on these algorithms can account for the selection bias and the full covariance structure implied by the spatial correlation. We illustrate the implementation of these algorithms through a simulation study.","downloadable_attachments":[{"id":83093697,"asset_id":75248352,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":105887119,"first_name":"Osman Burak","last_name":"Doğan","domain_name":"independent","page_name":"OsmanBurakDoğan","display_name":"Osman Burak Doğan","profile_url":"https://independent.academia.edu/OsmanBurakDo%C4%9Fan?f_ri=1032783","photo":"https://0.academia-photos.com/105887119/28222227/26425603/s65_osman_burak.do_an.jpg"}],"research_interests":[{"id":724,"name":"Economics","url":"https://www.academia.edu/Documents/in/Economics?f_ri=1032783","nofollow":true},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783","nofollow":true},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_75146751" data-work_id="75146751" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/75146751/A_Bayesian_Nonparametric_Multiple_Testing_Procedure_for_Comparing_Several_Treatments_Against_a_Control">A Bayesian Nonparametric Multiple Testing Procedure for Comparing Several Treatments Against a Control</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We propose a Bayesian nonparametric strategy to test for differences between a control group and several treatment regimes. Most of the existing tests for this type of comparison are based on the differences between location parameters.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_75146751" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We propose a Bayesian nonparametric strategy to test for differences between a control group and several treatment regimes. Most of the existing tests for this type of comparison are based on the differences between location parameters. In contrast, our approach identifies differences across the entire distribution, avoids strong modeling assumptions over the distributions for each treatment, and accounts for multiple testing through the prior distribution on the space of hypotheses. The proposal is compared to other commonly used hypothesis testing procedures under simulated scenarios. Two real applications are also analyzed with the proposed methodology.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/75146751" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="57e38e47f0f5684adba2667ef75dc220" rel="nofollow" data-download="{"attachment_id":83032972,"asset_id":75146751,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/83032972/download_file?st=MTc0MDUzMzUxMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="83461384" href="https://independent.academia.edu/JorgeDanielGonz%C3%A1lez">Jorge Daniel González</a><script data-card-contents-for-user="83461384" type="text/json">{"id":83461384,"first_name":"Jorge Daniel","last_name":"González","domain_name":"independent","page_name":"JorgeDanielGonzález","display_name":"Jorge Daniel González","profile_url":"https://independent.academia.edu/JorgeDanielGonz%C3%A1lez?f_ri=1032783","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_75146751 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="75146751"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 75146751, container: ".js-paper-rank-work_75146751", }); 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$(".js-view-count[data-work-id=75146751]").text(description); $(".js-view-count-work_75146751").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_75146751").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="75146751"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="3243" rel="nofollow" href="https://www.academia.edu/Documents/in/Nonparametric_Statistics">Nonparametric Statistics</a>, <script data-card-contents-for-ri="3243" type="text/json">{"id":3243,"name":"Nonparametric Statistics","url":"https://www.academia.edu/Documents/in/Nonparametric_Statistics?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="38701" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Analysis">Bayesian Analysis</a>, <script data-card-contents-for-ri="38701" type="text/json">{"id":38701,"name":"Bayesian Analysis","url":"https://www.academia.edu/Documents/in/Bayesian_Analysis?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1032783" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Probability">Bayesian Probability</a><script data-card-contents-for-ri="1032783" type="text/json">{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=75146751]'), work: {"id":75146751,"title":"A Bayesian Nonparametric Multiple Testing Procedure for Comparing Several Treatments Against a Control","created_at":"2022-04-01T05:14:21.812-07:00","url":"https://www.academia.edu/75146751/A_Bayesian_Nonparametric_Multiple_Testing_Procedure_for_Comparing_Several_Treatments_Against_a_Control?f_ri=1032783","dom_id":"work_75146751","summary":"We propose a Bayesian nonparametric strategy to test for differences between a control group and several treatment regimes. Most of the existing tests for this type of comparison are based on the differences between location parameters. In contrast, our approach identifies differences across the entire distribution, avoids strong modeling assumptions over the distributions for each treatment, and accounts for multiple testing through the prior distribution on the space of hypotheses. The proposal is compared to other commonly used hypothesis testing procedures under simulated scenarios. Two real applications are also analyzed with the proposed methodology.","downloadable_attachments":[{"id":83032972,"asset_id":75146751,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":83461384,"first_name":"Jorge Daniel","last_name":"González","domain_name":"independent","page_name":"JorgeDanielGonzález","display_name":"Jorge Daniel González","profile_url":"https://independent.academia.edu/JorgeDanielGonz%C3%A1lez?f_ri=1032783","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":3243,"name":"Nonparametric Statistics","url":"https://www.academia.edu/Documents/in/Nonparametric_Statistics?f_ri=1032783","nofollow":true},{"id":38701,"name":"Bayesian Analysis","url":"https://www.academia.edu/Documents/in/Bayesian_Analysis?f_ri=1032783","nofollow":true},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_75114178" data-work_id="75114178" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/75114178/On_the_fly_closed_loop_materials_discovery_via_Bayesian_active_learning">On-the-fly closed-loop materials discovery via Bayesian active learning</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_75114178" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron bea...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/75114178" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="797965717ff7e4c3e4c1b81b709455d4" rel="nofollow" data-download="{"attachment_id":83014219,"asset_id":75114178,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/83014219/download_file?st=MTc0MDUzMzUxMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="42955415" href="https://stanford.academia.edu/ApurvaMehta">Apurva Mehta</a><script data-card-contents-for-user="42955415" type="text/json">{"id":42955415,"first_name":"Apurva","last_name":"Mehta","domain_name":"stanford","page_name":"ApurvaMehta","display_name":"Apurva Mehta","profile_url":"https://stanford.academia.edu/ApurvaMehta?f_ri=1032783","photo":"https://0.academia-photos.com/42955415/45129712/35295054/s65_apurva.mehta.jpg"}</script></span></span></li><li class="js-paper-rank-work_75114178 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="75114178"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 75114178, container: ".js-paper-rank-work_75114178", }); 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In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron bea...","downloadable_attachments":[{"id":83014219,"asset_id":75114178,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":42955415,"first_name":"Apurva","last_name":"Mehta","domain_name":"stanford","page_name":"ApurvaMehta","display_name":"Apurva Mehta","profile_url":"https://stanford.academia.edu/ApurvaMehta?f_ri=1032783","photo":"https://0.academia-photos.com/42955415/45129712/35295054/s65_apurva.mehta.jpg"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1032783","nofollow":true},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine?f_ri=1032783","nofollow":true},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary?f_ri=1032783","nofollow":true},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"},{"id":1267800,"name":"Nature Communications","url":"https://www.academia.edu/Documents/in/Nature_Communications?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_74952543" data-work_id="74952543" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/74952543/Adaptive_traffic_control_system_based_on_Bayesian_probability_interpretation">Adaptive traffic control system based on Bayesian probability interpretation</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/74952543" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b5ee2c10bf671568640aa4cbbc334d6a" rel="nofollow" data-download="{"attachment_id":83628923,"asset_id":74952543,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button 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Shoukry","profile_url":"https://alex.academia.edu/AminShoukry?f_ri=1032783","photo":"https://0.academia-photos.com/23407956/7783619/8724270/s65_amin.shoukry.jpg"}</script></span></span></li><li class="js-paper-rank-work_74952543 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="74952543"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 74952543, container: ".js-paper-rank-work_74952543", }); });</script></li><li class="js-percentile-work_74952543 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 74952543; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = 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class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2534" rel="nofollow" href="https://www.academia.edu/Documents/in/Multiagent_Systems">Multiagent Systems</a><script data-card-contents-for-ri="2534" type="text/json">{"id":2534,"name":"Multiagent Systems","url":"https://www.academia.edu/Documents/in/Multiagent_Systems?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=74952543]'), work: {"id":74952543,"title":"Adaptive traffic control system based on Bayesian probability interpretation","created_at":"2022-03-30T01:53:01.416-07:00","url":"https://www.academia.edu/74952543/Adaptive_traffic_control_system_based_on_Bayesian_probability_interpretation?f_ri=1032783","dom_id":"work_74952543","summary":null,"downloadable_attachments":[{"id":83628923,"asset_id":74952543,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":23407956,"first_name":"Amin","last_name":"Shoukry","domain_name":"alex","page_name":"AminShoukry","display_name":"Amin Shoukry","profile_url":"https://alex.academia.edu/AminShoukry?f_ri=1032783","photo":"https://0.academia-photos.com/23407956/7783619/8724270/s65_amin.shoukry.jpg"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=1032783","nofollow":true},{"id":1688,"name":"Reinforcement Learning","url":"https://www.academia.edu/Documents/in/Reinforcement_Learning?f_ri=1032783","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1032783","nofollow":true},{"id":2534,"name":"Multiagent Systems","url":"https://www.academia.edu/Documents/in/Multiagent_Systems?f_ri=1032783","nofollow":true},{"id":8422,"name":"Adaptive Control","url":"https://www.academia.edu/Documents/in/Adaptive_Control?f_ri=1032783"},{"id":11592,"name":"System Dynamics","url":"https://www.academia.edu/Documents/in/System_Dynamics?f_ri=1032783"},{"id":15084,"name":"Statistical machine learning","url":"https://www.academia.edu/Documents/in/Statistical_machine_learning?f_ri=1032783"},{"id":22247,"name":"Traffic control","url":"https://www.academia.edu/Documents/in/Traffic_control?f_ri=1032783"},{"id":28846,"name":"Bayesian Probabilistic Analysis","url":"https://www.academia.edu/Documents/in/Bayesian_Probabilistic_Analysis?f_ri=1032783"},{"id":45873,"name":"Multi Agent System","url":"https://www.academia.edu/Documents/in/Multi_Agent_System?f_ri=1032783"},{"id":47295,"name":"Traffic Management","url":"https://www.academia.edu/Documents/in/Traffic_Management?f_ri=1032783"},{"id":85430,"name":"Multi Agent Systems","url":"https://www.academia.edu/Documents/in/Multi_Agent_Systems?f_ri=1032783"},{"id":109147,"name":"Bayesian methods","url":"https://www.academia.edu/Documents/in/Bayesian_methods?f_ri=1032783"},{"id":193953,"name":"Driver Behavior","url":"https://www.academia.edu/Documents/in/Driver_Behavior?f_ri=1032783"},{"id":201957,"name":"Acceleration","url":"https://www.academia.edu/Documents/in/Acceleration?f_ri=1032783"},{"id":436295,"name":"ROAD NETWORK","url":"https://www.academia.edu/Documents/in/ROAD_NETWORK?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"},{"id":1952679,"name":"Statistical Distributions","url":"https://www.academia.edu/Documents/in/Statistical_Distributions?f_ri=1032783"},{"id":2215017,"name":"Bayesian probability interpretation","url":"https://www.academia.edu/Documents/in/Bayesian_probability_interpretation?f_ri=1032783"},{"id":2814568,"name":"Probability Distribution","url":"https://www.academia.edu/Documents/in/Probability_Distribution?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_74908898" data-work_id="74908898" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/74908898/Bayesian_quantile_regression_joint_models_Inference_and_dynamic_predictions">Bayesian quantile regression joint models: Inference and dynamic predictions</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In the traditional joint models of a longitudinal and time-to-event outcome, a linear mixed model assuming normal random errors is used to model the longitudinal process. However, in many circumstances, the normality assumption is... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_74908898" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In the traditional joint models of a longitudinal and time-to-event outcome, a linear mixed model assuming normal random errors is used to model the longitudinal process. However, in many circumstances, the normality assumption is violated and the linear mixed model is not an appropriate sub-model in the joint models. In addition, as the linear mixed model models the conditional mean of the longitudinal outcome, it is not appropriate if clinical interest lies in making inference or prediction on median, lower, or upper ends of the longitudinal process. To this end, quantile regression provides a flexible, distribution-free way to study covariate effects at different quantiles of the longitudinal outcome and it is robust not only to deviation from normality, but also to outlying observations. In this article, we present and advocate the linear quantile mixed model for the longitudinal process in the joint models framework. Our development is motivated by a large prospective study of ...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/74908898" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="a893aa1a0cd5534f9d341a14374f36ed" rel="nofollow" data-download="{"attachment_id":82892186,"asset_id":74908898,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/82892186/download_file?st=MTc0MDUzMzUxMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="47706576" href="https://independent.academia.edu/StaciaDesantis">Stacia Desantis</a><script data-card-contents-for-user="47706576" type="text/json">{"id":47706576,"first_name":"Stacia","last_name":"Desantis","domain_name":"independent","page_name":"StaciaDesantis","display_name":"Stacia Desantis","profile_url":"https://independent.academia.edu/StaciaDesantis?f_ri=1032783","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_74908898 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="74908898"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 74908898, container: ".js-paper-rank-work_74908898", }); 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However, in many circumstances, the normality assumption is violated and the linear mixed model is not an appropriate sub-model in the joint models. In addition, as the linear mixed model models the conditional mean of the longitudinal outcome, it is not appropriate if clinical interest lies in making inference or prediction on median, lower, or upper ends of the longitudinal process. To this end, quantile regression provides a flexible, distribution-free way to study covariate effects at different quantiles of the longitudinal outcome and it is robust not only to deviation from normality, but also to outlying observations. In this article, we present and advocate the linear quantile mixed model for the longitudinal process in the joint models framework. Our development is motivated by a large prospective study of ...","downloadable_attachments":[{"id":82892186,"asset_id":74908898,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":47706576,"first_name":"Stacia","last_name":"Desantis","domain_name":"independent","page_name":"StaciaDesantis","display_name":"Stacia Desantis","profile_url":"https://independent.academia.edu/StaciaDesantis?f_ri=1032783","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":261,"name":"Geography","url":"https://www.academia.edu/Documents/in/Geography?f_ri=1032783","nofollow":true},{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=1032783","nofollow":true},{"id":18574,"name":"Inference","url":"https://www.academia.edu/Documents/in/Inference?f_ri=1032783","nofollow":true},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine?f_ri=1032783","nofollow":true},{"id":62081,"name":"Quantile Regression","url":"https://www.academia.edu/Documents/in/Quantile_Regression?f_ri=1032783"},{"id":410370,"name":"Public health systems and services research","url":"https://www.academia.edu/Documents/in/Public_health_systems_and_services_research-1?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_74674735" data-work_id="74674735" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/74674735/A_Bayesian_Prediction_of_the_Generalized_Pareto_Model">A Bayesian Prediction of the Generalized Pareto Model</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Rainfall weather patterns have changed due to global warming and sudden heavy rainfalls have become more frequent. Economic loss due to heavy rainfall has increased. We study the generalized Pareto distribution for modelling rainfall in... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_74674735" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Rainfall weather patterns have changed due to global warming and sudden heavy rainfalls have become more frequent. Economic loss due to heavy rainfall has increased. We study the generalized Pareto distribution for modelling rainfall in Seoul based on data from 1973 to 2008. We use several priors including Jeffrey's noninformative prior and Gibbs sampling method to derive Bayesian posterior predictive distributions. The probability of heavy rainfall has increased over the last ten years based on estimated posterior predictive distribution.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/74674735" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="90995088e63cb9667e830d46bab3482f" rel="nofollow" data-download="{"attachment_id":82741347,"asset_id":74674735,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/82741347/download_file?st=MTc0MDUzMzUxMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="107577260" href="https://handong.academia.edu/%EA%B5%90%EC%88%98%EB%8B%98%EC%86%90%EC%A4%91%EA%B6%8C">교수님 손중권</a><script data-card-contents-for-user="107577260" type="text/json">{"id":107577260,"first_name":"교수님","last_name":"손중권","domain_name":"handong","page_name":"교수님손중권","display_name":"교수님 손중권","profile_url":"https://handong.academia.edu/%EA%B5%90%EC%88%98%EB%8B%98%EC%86%90%EC%A4%91%EA%B6%8C?f_ri=1032783","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_74674735 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="74674735"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 74674735, container: ".js-paper-rank-work_74674735", }); 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Economic loss due to heavy rainfall has increased. We study the generalized Pareto distribution for modelling rainfall in Seoul based on data from 1973 to 2008. We use several priors including Jeffrey's noninformative prior and Gibbs sampling method to derive Bayesian posterior predictive distributions. The probability of heavy rainfall has increased over the last ten years based on estimated posterior predictive distribution.","downloadable_attachments":[{"id":82741347,"asset_id":74674735,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":107577260,"first_name":"교수님","last_name":"손중권","domain_name":"handong","page_name":"교수님손중권","display_name":"교수님 손중권","profile_url":"https://handong.academia.edu/%EA%B5%90%EC%88%98%EB%8B%98%EC%86%90%EC%A4%91%EA%B6%8C?f_ri=1032783","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=1032783","nofollow":true},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_72497354" data-work_id="72497354" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/72497354/Denying_antecedents_and_affirming_consequents_The_state_of_the_art">Denying antecedents and affirming consequents: The state of the art</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Recent work on conditional reasoning argues that denying the antecedent [DA] and affirming the consequent [AC] are defeasible but cogent patterns of argument, either because they are effective, rational, albeit heuristic applications of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_72497354" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Recent work on conditional reasoning argues that denying the antecedent [DA] and affirming the consequent [AC] are defeasible but cogent patterns of argument, either because they are effective, rational, albeit heuristic applications of Bayesian probability, or because they are licensed by the principle of total evidence. Against this, we show that on any prevailing interpretation of indicative conditionals the premises of DA and AC arguments do not license their conclusions without additional assumptions. The cogency of DA and AC inferences rather depends on contingent factors extrinsic to, and independent of, what is asserted by DA and AC arguments.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/72497354" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="3780031" href="https://nankai.academia.edu/FrankZenker">Frank Zenker</a><script data-card-contents-for-user="3780031" type="text/json">{"id":3780031,"first_name":"Frank","last_name":"Zenker","domain_name":"nankai","page_name":"FrankZenker","display_name":"Frank Zenker","profile_url":"https://nankai.academia.edu/FrankZenker?f_ri=1032783","photo":"https://0.academia-photos.com/3780031/1370616/16541614/s65_frank.zenker.jpg"}</script></span></span></li><li class="js-paper-rank-work_72497354 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="72497354"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 72497354, container: ".js-paper-rank-work_72497354", }); 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Against this, we show that on any prevailing interpretation of indicative conditionals the premises of DA and AC arguments do not license their conclusions without additional assumptions. The cogency of DA and AC inferences rather depends on contingent factors extrinsic to, and independent of, what is asserted by DA and AC arguments.","downloadable_attachments":[],"ordered_authors":[{"id":3780031,"first_name":"Frank","last_name":"Zenker","domain_name":"nankai","page_name":"FrankZenker","display_name":"Frank Zenker","profile_url":"https://nankai.academia.edu/FrankZenker?f_ri=1032783","photo":"https://0.academia-photos.com/3780031/1370616/16541614/s65_frank.zenker.jpg"}],"research_interests":[{"id":803,"name":"Philosophy","url":"https://www.academia.edu/Documents/in/Philosophy?f_ri=1032783","nofollow":true},{"id":2537,"name":"Heuristics","url":"https://www.academia.edu/Documents/in/Heuristics?f_ri=1032783","nofollow":true},{"id":3244,"name":"Bayesian","url":"https://www.academia.edu/Documents/in/Bayesian?f_ri=1032783","nofollow":true},{"id":8612,"name":"Argumentation","url":"https://www.academia.edu/Documents/in/Argumentation?f_ri=1032783","nofollow":true},{"id":22254,"name":"Cognitive Bias","url":"https://www.academia.edu/Documents/in/Cognitive_Bias?f_ri=1032783"},{"id":23036,"name":"Argumentation Theory","url":"https://www.academia.edu/Documents/in/Argumentation_Theory?f_ri=1032783"},{"id":28299,"name":"Argumentation Theory and Critical Thinking","url":"https://www.academia.edu/Documents/in/Argumentation_Theory_and_Critical_Thinking?f_ri=1032783"},{"id":33324,"name":"Informal Logic","url":"https://www.academia.edu/Documents/in/Informal_Logic?f_ri=1032783"},{"id":51282,"name":"Fallacies","url":"https://www.academia.edu/Documents/in/Fallacies?f_ri=1032783"},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783"},{"id":116537,"name":"Heuristics and Biases","url":"https://www.academia.edu/Documents/in/Heuristics_and_Biases?f_ri=1032783"},{"id":187987,"name":"Conversational Implicatures","url":"https://www.academia.edu/Documents/in/Conversational_Implicatures?f_ri=1032783"},{"id":366200,"name":"Principle of total evidence","url":"https://www.academia.edu/Documents/in/Principle_of_total_evidence?f_ri=1032783"},{"id":711508,"name":"Logical Fallacies","url":"https://www.academia.edu/Documents/in/Logical_Fallacies?f_ri=1032783"},{"id":973438,"name":"Fallacy","url":"https://www.academia.edu/Documents/in/Fallacy?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"},{"id":1808204,"name":"Denying the Antecedent","url":"https://www.academia.edu/Documents/in/Denying_the_Antecedent?f_ri=1032783"},{"id":1840639,"name":"Conditional Perfection","url":"https://www.academia.edu/Documents/in/Conditional_Perfection?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_82651008" data-work_id="82651008" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/82651008/Using_SPM_12s_Second_Level_Bayesian_Inference_Procedure_for_fMRI_Analysis_Practical_Guidelines_for_End_Users">Using SPM 12's Second-Level Bayesian Inference Procedure for fMRI Analysis: Practical Guidelines for End Users</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Recent debates about the conventional traditional threshold used in the fields of neuroscience and psychology, namely&lt; 0.05, have spurred researchers to consider alternative ways to analyze fMRI data. A group of methodologists and... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_82651008" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Recent debates about the conventional traditional threshold used in the fields of neuroscience and psychology, namely&lt; 0.05, have spurred researchers to consider alternative ways to analyze fMRI data. A group of methodologists and statisticians have considered Bayesian inference as a candidate methodology. However, few previous studies have attempted to provide end users of fMRI analysis tools, such as SPM 12, with practical guidelines about how to conduct Bayesian inference. In the present study, we aim to demonstrate how to utilize Bayesian inference, Bayesian second-level inference in particular, implemented in SPM 12 by analyzing fMRI data available to public via NeuroVault. In addition, to help end users understand how Bayesian inference actually works in SPM 12, we examine outcomes from Bayesian second-level inference implemented in SPM 12 by comparing them with those from classical second-level inference. Finally, we provide practical guidelines about how to set the parame...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/82651008" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ee73dcc34129df36217c8f68df393b30" rel="nofollow" data-download="{"attachment_id":88288464,"asset_id":82651008,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/88288464/download_file?st=MTc0MDUzMzUxMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="3872234" href="https://alabama.academia.edu/HyeminHan">Hyemin Han</a><script data-card-contents-for-user="3872234" type="text/json">{"id":3872234,"first_name":"Hyemin","last_name":"Han","domain_name":"alabama","page_name":"HyeminHan","display_name":"Hyemin Han","profile_url":"https://alabama.academia.edu/HyeminHan?f_ri=1032783","photo":"https://0.academia-photos.com/3872234/1427255/20905176/s65_hyemin.han.jpg"}</script></span></span></li><li class="js-paper-rank-work_82651008 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="82651008"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 82651008, container: ".js-paper-rank-work_82651008", }); 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$(".js-view-count[data-work-id=82651008]").text(description); $(".js-view-count-work_82651008").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_82651008").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="82651008"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">20</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="236" rel="nofollow" href="https://www.academia.edu/Documents/in/Cognitive_Psychology">Cognitive Psychology</a>, <script data-card-contents-for-ri="236" type="text/json">{"id":236,"name":"Cognitive Psychology","url":"https://www.academia.edu/Documents/in/Cognitive_Psychology?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1613" rel="nofollow" href="https://www.academia.edu/Documents/in/Brain_Imaging">Brain Imaging</a>, <script data-card-contents-for-ri="1613" type="text/json">{"id":1613,"name":"Brain Imaging","url":"https://www.academia.edu/Documents/in/Brain_Imaging?f_ri=1032783","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a><script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1032783","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=82651008]'), work: {"id":82651008,"title":"Using SPM 12's Second-Level Bayesian Inference Procedure for fMRI Analysis: Practical Guidelines for End Users","created_at":"2022-07-05T09:51:15.445-07:00","url":"https://www.academia.edu/82651008/Using_SPM_12s_Second_Level_Bayesian_Inference_Procedure_for_fMRI_Analysis_Practical_Guidelines_for_End_Users?f_ri=1032783","dom_id":"work_82651008","summary":"Recent debates about the conventional traditional threshold used in the fields of neuroscience and psychology, namely\u0026lt; 0.05, have spurred researchers to consider alternative ways to analyze fMRI data. A group of methodologists and statisticians have considered Bayesian inference as a candidate methodology. However, few previous studies have attempted to provide end users of fMRI analysis tools, such as SPM 12, with practical guidelines about how to conduct Bayesian inference. In the present study, we aim to demonstrate how to utilize Bayesian inference, Bayesian second-level inference in particular, implemented in SPM 12 by analyzing fMRI data available to public via NeuroVault. In addition, to help end users understand how Bayesian inference actually works in SPM 12, we examine outcomes from Bayesian second-level inference implemented in SPM 12 by comparing them with those from classical second-level inference. Finally, we provide practical guidelines about how to set the parame...","downloadable_attachments":[{"id":88288464,"asset_id":82651008,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":3872234,"first_name":"Hyemin","last_name":"Han","domain_name":"alabama","page_name":"HyeminHan","display_name":"Hyemin Han","profile_url":"https://alabama.academia.edu/HyeminHan?f_ri=1032783","photo":"https://0.academia-photos.com/3872234/1427255/20905176/s65_hyemin.han.jpg"}],"research_interests":[{"id":236,"name":"Cognitive Psychology","url":"https://www.academia.edu/Documents/in/Cognitive_Psychology?f_ri=1032783","nofollow":true},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true},{"id":1613,"name":"Brain Imaging","url":"https://www.academia.edu/Documents/in/Brain_Imaging?f_ri=1032783","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1032783","nofollow":true},{"id":3244,"name":"Bayesian","url":"https://www.academia.edu/Documents/in/Bayesian?f_ri=1032783"},{"id":4060,"name":"Applied Statistics","url":"https://www.academia.edu/Documents/in/Applied_Statistics?f_ri=1032783"},{"id":8780,"name":"Cognitive Neuropsychology","url":"https://www.academia.edu/Documents/in/Cognitive_Neuropsychology?f_ri=1032783"},{"id":9088,"name":"Affective Neuroscience","url":"https://www.academia.edu/Documents/in/Affective_Neuroscience?f_ri=1032783"},{"id":21548,"name":"Cognitive Neuroscience","url":"https://www.academia.edu/Documents/in/Cognitive_Neuroscience?f_ri=1032783"},{"id":25395,"name":"Matlab","url":"https://www.academia.edu/Documents/in/Matlab?f_ri=1032783"},{"id":29917,"name":"FMRI","url":"https://www.academia.edu/Documents/in/FMRI?f_ri=1032783"},{"id":30601,"name":"Behavioral Neuroscience","url":"https://www.academia.edu/Documents/in/Behavioral_Neuroscience?f_ri=1032783"},{"id":38701,"name":"Bayesian Analysis","url":"https://www.academia.edu/Documents/in/Bayesian_Analysis?f_ri=1032783"},{"id":48641,"name":"Applied Bayesian Statistics","url":"https://www.academia.edu/Documents/in/Applied_Bayesian_Statistics?f_ri=1032783"},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783"},{"id":52176,"name":"Brain Mapping","url":"https://www.academia.edu/Documents/in/Brain_Mapping?f_ri=1032783"},{"id":61474,"name":"Brain","url":"https://www.academia.edu/Documents/in/Brain?f_ri=1032783"},{"id":97765,"name":"Functional Magnetic Resonance Imaging","url":"https://www.academia.edu/Documents/in/Functional_Magnetic_Resonance_Imaging?f_ri=1032783"},{"id":100094,"name":"Bayesian statistics","url":"https://www.academia.edu/Documents/in/Bayesian_statistics?f_ri=1032783"},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_82378180" data-work_id="82378180" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/82378180/Extended_Bayesian_inference_incorporating_symmetry_bias">Extended Bayesian inference incorporating symmetry bias</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this study, we start by proposing a causal induction model that incorporates symmetry bias. This model has two parameters that control the strength of symmetry bias and includes conditional probability and conventional models of causal... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_82378180" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this study, we start by proposing a causal induction model that incorporates symmetry bias. This model has two parameters that control the strength of symmetry bias and includes conditional probability and conventional models of causal induction as special cases. It can reproduce causal induction of human judgment with high accuracy. We further propose a human-like Bayesian inference method to replace the conditional probability in Bayesian inference with the aforementioned causal induction model. In this method, two components coexist: the component of Bayesian inference, which updates the degree of confidence for each hypothesis, and the component of inverse Bayesian inference that modifies the model of each hypothesis. In other words, this method allows not only inference but also simultaneous learning. Our study demonstrates that the method with both Bayesian inference and inverse Bayesian inference enables us to deal flexibly with unsteady situations where the target of infe...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/82378180" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b998bf07b2bb46afd57b96cb99599009" rel="nofollow" data-download="{"attachment_id":88106784,"asset_id":82378180,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/88106784/download_file?st=MTc0MDUzMzUxMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="31778684" href="https://independent.academia.edu/ShujiShinohara">Shuji S Shinohara</a><script data-card-contents-for-user="31778684" type="text/json">{"id":31778684,"first_name":"Shuji","last_name":"Shinohara","domain_name":"independent","page_name":"ShujiShinohara","display_name":"Shuji S Shinohara","profile_url":"https://independent.academia.edu/ShujiShinohara?f_ri=1032783","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_82378180 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="82378180"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 82378180, container: ".js-paper-rank-work_82378180", }); 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This model has two parameters that control the strength of symmetry bias and includes conditional probability and conventional models of causal induction as special cases. It can reproduce causal induction of human judgment with high accuracy. We further propose a human-like Bayesian inference method to replace the conditional probability in Bayesian inference with the aforementioned causal induction model. In this method, two components coexist: the component of Bayesian inference, which updates the degree of confidence for each hypothesis, and the component of inverse Bayesian inference that modifies the model of each hypothesis. In other words, this method allows not only inference but also simultaneous learning. Our study demonstrates that the method with both Bayesian inference and inverse Bayesian inference enables us to deal flexibly with unsteady situations where the target of infe...","downloadable_attachments":[{"id":88106784,"asset_id":82378180,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":31778684,"first_name":"Shuji","last_name":"Shinohara","domain_name":"independent","page_name":"ShujiShinohara","display_name":"Shuji S Shinohara","profile_url":"https://independent.academia.edu/ShujiShinohara?f_ri=1032783","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=1032783","nofollow":true},{"id":422,"name":"Computer 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u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/81926013/Introduction_to_Bayesian_Data_Analysis">Introduction to Bayesian Data Analysis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Using R for predictive model checking Multinomial Model conjugate analysis and its application in R Comparing classical and Bayesian multiparameter models Multivariate Normal Models Complex Contingency Tables Example: Regression modeling... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_81926013" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Using R for predictive model checking Multinomial Model conjugate analysis and its application in R Comparing classical and Bayesian multiparameter models Multivariate Normal Models Complex Contingency Tables Example: Regression modeling with change point On the night of January 27, 1986, the night before the space shuttle Challenger accident, there was a tree-hour teleconference among people at Marton Thiokol</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/81926013" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0407eecf3dc42cbd5dd0cf5b65e1bb02" rel="nofollow" data-download="{"attachment_id":87798994,"asset_id":81926013,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen 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multiparameter models Multivariate Normal Models Complex Contingency Tables Example: Regression modeling with change point On the night of January 27, 1986, the night before the space shuttle Challenger accident, there was a tree-hour teleconference among people at Marton Thiokol","downloadable_attachments":[{"id":87798994,"asset_id":81926013,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":37295732,"first_name":"David","last_name":"Draper","domain_name":"independent","page_name":"DavidDraper3","display_name":"David Draper","profile_url":"https://independent.academia.edu/DavidDraper3?f_ri=1032783","photo":"https://0.academia-photos.com/37295732/32411395/32080288/s65_david.draper.jpg"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1032783","nofollow":true},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=1032783","nofollow":true},{"id":1032783,"name":"Bayesian Probability","url":"https://www.academia.edu/Documents/in/Bayesian_Probability?f_ri=1032783","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_81859643" data-work_id="81859643" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/81859643/PicHunter_Bayesian_relevance_feedback_for_image_retrieval">PicHunter: Bayesian relevance feedback for image retrieval</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper describes PicHunter, an image retrieval sys tem that implements a novel approach to relevance feedback. It uses Bayesian learning based on a probabilistic model of a user's behavior. The predictions of this model are com bined... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_81859643" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper describes PicHunter, an image retrieval sys tem that implements a novel approach to relevance feedback. It uses Bayesian learning based on a probabilistic model of a user's behavior. The predictions of this model are com bined with the selections made during a search to choose the images to display. The details of our model were tuned using an offline learning algorithm. For clarity, our studies were done with the simplest possible user interface but the algorithm can easily be incorporated into a system which supports complex queries. Even with this constraint and simple image features, PicHunter is able to locate randomly selected targets in a database of-l5!!!! images after display ing an average of only 55 groups of-l images which is over 10 times better than chance.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/81859643" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="9c8152ebe5b3bf8e3624c9daacffbb6e" rel="nofollow" data-download="{"attachment_id":87756865,"asset_id":81859643,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/87756865/download_file?st=MTc0MDUzMzUxMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="7433346" href="https://independent.academia.edu/SteveOmohundro">Steve Omohundro</a><script data-card-contents-for-user="7433346" type="text/json">{"id":7433346,"first_name":"Steve","last_name":"Omohundro","domain_name":"independent","page_name":"SteveOmohundro","display_name":"Steve Omohundro","profile_url":"https://independent.academia.edu/SteveOmohundro?f_ri=1032783","photo":"https://gravatar.com/avatar/330502dd467a11bdeb1d9970289291e6?s=65"}</script></span></span></li><li class="js-paper-rank-work_81859643 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="81859643"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 81859643, container: ".js-paper-rank-work_81859643", }); 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It uses Bayesian learning based on a probabilistic model of a user's behavior. The predictions of this model are com bined with the selections made during a search to choose the images to display. The details of our model were tuned using an offline learning algorithm. For clarity, our studies were done with the simplest possible user interface but the algorithm can easily be incorporated into a system which supports complex queries. 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