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optimization</title> <meta name="description" content="Search results for: bayesian optimization"> <meta name="keywords" content="bayesian optimization"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="bayesian optimization"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 3526</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: bayesian optimization</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3526</span> Bayesian Optimization for Reaction Parameter Tuning: An Exploratory Study of Parameter Optimization in Oxidative Desulfurization of Thiophene</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aman%20Sharma">Aman Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Sonali%20Sengupta"> Sonali Sengupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study explores the utility of Bayesian optimization in tuning the physical and chemical parameters of reactions in an offline experimental setup. A comparative analysis of the influence of the acquisition function on the optimization performance is also studied. For proxy first and second-order reactions, the results are indifferent to the acquisition function used, whereas, while studying the parameters for oxidative desulphurization of thiophene in an offline setup, upper confidence bound (UCB) provides faster convergence along with a marginal trade-off in the maximum conversion achieved. The work also demarcates the critical number of independent parameters and input observations required for both sequential and offline reaction setups to yield tangible results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acquisition%20function" title="acquisition function">acquisition function</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20optimization" title=" Bayesian optimization"> Bayesian optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=desulfurization" title=" desulfurization"> desulfurization</a>, <a href="https://publications.waset.org/abstracts/search?q=kinetics" title=" kinetics"> kinetics</a>, <a href="https://publications.waset.org/abstracts/search?q=thiophene" title=" thiophene"> thiophene</a> </p> <a href="https://publications.waset.org/abstracts/135023/bayesian-optimization-for-reaction-parameter-tuning-an-exploratory-study-of-parameter-optimization-in-oxidative-desulfurization-of-thiophene" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135023.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">182</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3525</span> Networked Implementation of Milling Stability Optimization with Bayesian Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Christoph%20Ramsauer">Christoph Ramsauer</a>, <a href="https://publications.waset.org/abstracts/search?q=Jaydeep%20Karandikar"> Jaydeep Karandikar</a>, <a href="https://publications.waset.org/abstracts/search?q=Tony%20Schmitz"> Tony Schmitz</a>, <a href="https://publications.waset.org/abstracts/search?q=Friedrich%20Bleicher"> Friedrich Bleicher</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machining stability is an important limitation to discrete part machining. In this work, a networked implementation of milling stability optimization with Bayesian learning is presented. The milling process was monitored with a wireless sensory tool holder instrumented with an accelerometer at the Vienna University of Technology, Vienna, Austria. The recorded data from a milling test cut is used to classify the cut as stable or unstable based on the frequency analysis. The test cut result is fed to a Bayesian stability learning algorithm at the University of Tennessee, Knoxville, Tennessee, USA. The algorithm calculates the probability of stability as a function of axial depth of cut and spindle speed and recommends the parameters for the next test cut. The iterative process between two transatlantic locations repeats until convergence to a stable optimal process parameter set is achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machining%20stability" title="machining stability">machining stability</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor" title=" sensor"> sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/135659/networked-implementation-of-milling-stability-optimization-with-bayesian-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135659.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">206</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3524</span> Factorization of Computations in Bayesian Networks: Interpretation of Factors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Linda%20Smail">Linda Smail</a>, <a href="https://publications.waset.org/abstracts/search?q=Zineb%20Azouz"> Zineb Azouz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Given a Bayesian network relative to a set I of discrete random variables, we are interested in computing the probability distribution P(S) where S is a subset of I. The general idea is to write the expression of P(S) in the form of a product of factors where each factor is easy to compute. More importantly, it will be very useful to give an interpretation of each of the factors in terms of conditional probabilities. This paper considers a semantic interpretation of the factors involved in computing marginal probabilities in Bayesian networks. Establishing such a semantic interpretations is indeed interesting and relevant in the case of large Bayesian networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20networks" title="Bayesian networks">Bayesian networks</a>, <a href="https://publications.waset.org/abstracts/search?q=D-Separation" title=" D-Separation"> D-Separation</a>, <a href="https://publications.waset.org/abstracts/search?q=level%20two%20Bayesian%20networks" title=" level two Bayesian networks"> level two Bayesian networks</a>, <a href="https://publications.waset.org/abstracts/search?q=factorization%20of%20computation" title=" factorization of computation"> factorization of computation</a> </p> <a href="https://publications.waset.org/abstracts/18829/factorization-of-computations-in-bayesian-networks-interpretation-of-factors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18829.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">529</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3523</span> Probabilistic Approach of Dealing with Uncertainties in Distributed Constraint Optimization Problems and Situation Awareness for Multi-agent Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sagir%20M.%20Yusuf">Sagir M. Yusuf</a>, <a href="https://publications.waset.org/abstracts/search?q=Chris%20Baber"> Chris Baber</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DCOP" title="DCOP">DCOP</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent%20reasoning" title=" multi-agent reasoning"> multi-agent reasoning</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20reasoning" title=" Bayesian reasoning"> Bayesian reasoning</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20intelligence" title=" swarm intelligence"> swarm intelligence</a> </p> <a href="https://publications.waset.org/abstracts/116869/probabilistic-approach-of-dealing-with-uncertainties-in-distributed-constraint-optimization-problems-and-situation-awareness-for-multi-agent-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116869.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">119</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3522</span> Bayesian Analysis of Topp-Leone Generalized Exponential Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Najrullah%20Khan">Najrullah Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Athar%20Ali%20Khan"> Athar Ali Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Topp-Leone distribution was introduced by Topp- Leone in 1955. In this paper, an attempt has been made to fit Topp-Leone Generalized exponential (TPGE) distribution. A real survival data set is used for illustrations. Implementation is done using R and JAGS and appropriate illustrations are made. R and JAGS codes have been provided to implement censoring mechanism using both optimization and simulation tools. The main aim of this paper is to describe and illustrate the Bayesian modelling approach to the analysis of survival data. Emphasis is placed on the modeling of data and the interpretation of the results. Crucial to this is an understanding of the nature of the incomplete or 'censored' data encountered. Analytic approximation and simulation tools are covered here, but most of the emphasis is on Markov chain based Monte Carlo method including independent Metropolis algorithm, which is currently the most popular technique. For analytic approximation, among various optimization algorithms and trust region method is found to be the best. In this paper, TPGE model is also used to analyze the lifetime data in Bayesian paradigm. Results are evaluated from the above mentioned real survival data set. The analytic approximation and simulation methods are implemented using some software packages. It is clear from our findings that simulation tools provide better results as compared to those obtained by asymptotic approximation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20Inference" title="Bayesian Inference">Bayesian Inference</a>, <a href="https://publications.waset.org/abstracts/search?q=JAGS" title=" JAGS"> JAGS</a>, <a href="https://publications.waset.org/abstracts/search?q=Laplace%20Approximation" title=" Laplace Approximation"> Laplace Approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=LaplacesDemon" title=" LaplacesDemon"> LaplacesDemon</a>, <a href="https://publications.waset.org/abstracts/search?q=posterior" title=" posterior"> posterior</a>, <a href="https://publications.waset.org/abstracts/search?q=R%20Software" title=" R Software"> R Software</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/77532/bayesian-analysis-of-topp-leone-generalized-exponential-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77532.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">535</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3521</span> Optimal Bayesian Control of the Proportion of Defectives in a Manufacturing Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Viliam%20Makis">Viliam Makis</a>, <a href="https://publications.waset.org/abstracts/search?q=Farnoosh%20Naderkhani"> Farnoosh Naderkhani</a>, <a href="https://publications.waset.org/abstracts/search?q=Leila%20Jafari"> Leila Jafari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a model and an algorithm for the calculation of the optimal control limit, average cost, sample size, and the sampling interval for an optimal Bayesian chart to control the proportion of defective items produced using a semi-Markov decision process approach. Traditional p-chart has been widely used for controlling the proportion of defectives in various kinds of production processes for many years. It is well known that traditional non-Bayesian charts are not optimal, but very few optimal Bayesian control charts have been developed in the literature, mostly considering finite horizon. The objective of this paper is to develop a fast computational algorithm to obtain the optimal parameters of a Bayesian p-chart. The decision problem is formulated in the partially observable framework and the developed algorithm is illustrated by a numerical example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20control%20chart" title="Bayesian control chart">Bayesian control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-Markov%20decision%20process" title=" semi-Markov decision process"> semi-Markov decision process</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20control" title=" quality control"> quality control</a>, <a href="https://publications.waset.org/abstracts/search?q=partially%20observable%20process" title=" partially observable process"> partially observable process</a> </p> <a href="https://publications.waset.org/abstracts/49751/optimal-bayesian-control-of-the-proportion-of-defectives-in-a-manufacturing-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49751.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">318</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3520</span> Improved Dynamic Bayesian Networks Applied to Arabic On Line Characters Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Redouane%20Tlemsani">Redouane Tlemsani</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelkader%20Benyettou"> Abdelkader Benyettou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Work is in on line Arabic character recognition and the principal motivation is to study the Arab manuscript with on line technology. This system is a Markovian system, which one can see as like a Dynamic Bayesian Network (DBN). One of the major interests of these systems resides in the complete models training (topology and parameters) starting from training data. Our approach is based on the dynamic Bayesian Networks formalism. The DBNs theory is a Bayesians networks generalization to the dynamic processes. Among our objective, amounts finding better parameters, which represent the links (dependences) between dynamic network variables. In applications in pattern recognition, one will carry out the fixing of the structure, which obliges us to admit some strong assumptions (for example independence between some variables). Our application will relate to the Arabic isolated characters on line recognition using our laboratory database: NOUN. A neural tester proposed for DBN external optimization. The DBN scores and DBN mixed are respectively 70.24% and 62.50%, which lets predict their further development; other approaches taking account time were considered and implemented until obtaining a significant recognition rate 94.79%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arabic%20on%20line%20character%20recognition" title="Arabic on line character recognition">Arabic on line character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20Bayesian%20network" title=" dynamic Bayesian network"> dynamic Bayesian network</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a> </p> <a href="https://publications.waset.org/abstracts/7319/improved-dynamic-bayesian-networks-applied-to-arabic-on-line-characters-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7319.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">428</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3519</span> The Effect of Institutions on Economic Growth: An Analysis Based on Bayesian Panel Data Estimation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Anwar">Mohammad Anwar</a>, <a href="https://publications.waset.org/abstracts/search?q=Shah%20Waliullah"> Shah Waliullah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study investigated panel data regression models. This paper used Bayesian and classical methods to study the impact of institutions on economic growth from data (1990-2014), especially in developing countries. Under the classical and Bayesian methodology, the two-panel data models were estimated, which are common effects and fixed effects. For the Bayesian approach, the prior information is used in this paper, and normal gamma prior is used for the panel data models. The analysis was done through WinBUGS14 software. The estimated results of the study showed that panel data models are valid models in Bayesian methodology. In the Bayesian approach, the effects of all independent variables were positively and significantly affected by the dependent variables. Based on the standard errors of all models, we must say that the fixed effect model is the best model in the Bayesian estimation of panel data models. Also, it was proved that the fixed effect model has the lowest value of standard error, as compared to other models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20approach" title="Bayesian approach">Bayesian approach</a>, <a href="https://publications.waset.org/abstracts/search?q=common%20effect" title=" common effect"> common effect</a>, <a href="https://publications.waset.org/abstracts/search?q=fixed%20effect" title=" fixed effect"> fixed effect</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20effect" title=" random effect"> random effect</a>, <a href="https://publications.waset.org/abstracts/search?q=Dynamic%20Random%20Effect%20Model" title=" Dynamic Random Effect Model"> Dynamic Random Effect Model</a> </p> <a href="https://publications.waset.org/abstracts/161692/the-effect-of-institutions-on-economic-growth-an-analysis-based-on-bayesian-panel-data-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161692.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">68</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3518</span> Bayesian Approach for Moving Extremes Ranked Set Sampling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Said%20Ali%20Al-Hadhrami">Said Ali Al-Hadhrami</a>, <a href="https://publications.waset.org/abstracts/search?q=Amer%20Ibrahim%20Al-Omari"> Amer Ibrahim Al-Omari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, Bayesian estimation for the mean of exponential distribution is considered using Moving Extremes Ranked Set Sampling (MERSS). Three priors are used; Jeffery, conjugate and constant using MERSS and Simple Random Sampling (SRS). Some properties of the proposed estimators are investigated. It is found that the suggested estimators using MERSS are more efficient than its counterparts based on SRS. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian" title="Bayesian">Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=efficiency" title=" efficiency"> efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=moving%20extreme%20ranked%20set%20sampling" title=" moving extreme ranked set sampling"> moving extreme ranked set sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=ranked%20set%20sampling" title=" ranked set sampling"> ranked set sampling</a> </p> <a href="https://publications.waset.org/abstracts/30733/bayesian-approach-for-moving-extremes-ranked-set-sampling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30733.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">513</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3517</span> Enhancing Predictive Accuracy in Pharmaceutical Sales through an Ensemble Kernel Gaussian Process Regression Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shahin%20Mirshekari">Shahin Mirshekari</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammadreza%20Moradi"> Mohammadreza Moradi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Jafari"> Hossein Jafari</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehdi%20Jafari"> Mehdi Jafari</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Ensaf"> Mohammad Ensaf</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research employs Gaussian Process Regression (GPR) with an ensemble kernel, integrating Exponential Squared, Revised Matern, and Rational Quadratic kernels to analyze pharmaceutical sales data. Bayesian optimization was used to identify optimal kernel weights: 0.76 for Exponential Squared, 0.21 for Revised Matern, and 0.13 for Rational Quadratic. The ensemble kernel demonstrated superior performance in predictive accuracy, achieving an R² score near 1.0, and significantly lower values in MSE, MAE, and RMSE. These findings highlight the efficacy of ensemble kernels in GPR for predictive analytics in complex pharmaceutical sales datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20regression" title="Gaussian process regression">Gaussian process regression</a>, <a href="https://publications.waset.org/abstracts/search?q=ensemble%20kernels" title=" ensemble kernels"> ensemble kernels</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian%20optimization" title=" bayesian optimization"> bayesian optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=pharmaceutical%20sales%20analysis" title=" pharmaceutical sales analysis"> pharmaceutical sales analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20forecasting" title=" time series forecasting"> time series forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a> </p> <a href="https://publications.waset.org/abstracts/181581/enhancing-predictive-accuracy-in-pharmaceutical-sales-through-an-ensemble-kernel-gaussian-process-regression-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/181581.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">71</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3516</span> Bayesian Reliability of Weibull Regression with Type-I Censored Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Al%20Omari%20Moahmmed%20Ahmed">Al Omari Moahmmed Ahmed </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the Bayesian, we developed an approach by using non-informative prior with covariate and obtained by using Gauss quadrature method to estimate the parameters of the covariate and reliability function of the Weibull regression distribution with Type-I censored data. The maximum likelihood seen that the estimators obtained are not available in closed forms, although they can be solved it by using Newton-Raphson methods. The comparison criteria are the MSE and the performance of these estimates are assessed using simulation considering various sample size, several specific values of shape parameter. The results show that Bayesian with non-informative prior is better than Maximum Likelihood Estimator. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=non-informative%20prior" title="non-informative prior">non-informative prior</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20method" title=" Bayesian method"> Bayesian method</a>, <a href="https://publications.waset.org/abstracts/search?q=type-I%20censoring" title=" type-I censoring"> type-I censoring</a>, <a href="https://publications.waset.org/abstracts/search?q=Gauss%20quardature" title=" Gauss quardature"> Gauss quardature</a> </p> <a href="https://publications.waset.org/abstracts/18728/bayesian-reliability-of-weibull-regression-with-type-i-censored-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18728.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">503</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3515</span> Identification of Bayesian Network with Convolutional Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Raouf%20Benmakrelouf">Mohamed Raouf Benmakrelouf</a>, <a href="https://publications.waset.org/abstracts/search?q=Wafa%20Karouche"> Wafa Karouche</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Rynkiewicz"> Joseph Rynkiewicz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose an alternative method to construct a Bayesian Network (BN). This method relies on a convolutional neural network (CNN classifier), which determinates the edges of the network skeleton. We train a CNN on a normalized empirical probability density distribution (NEPDF) for predicting causal interactions and relationships. We have to find the optimal Bayesian network structure for causal inference. Indeed, we are undertaking a search for pair-wise causality, depending on considered causal assumptions. In order to avoid unreasonable causal structure, we consider a blacklist and a whitelist of causality senses. We tested the method on real data to assess the influence of education on the voting intention for the extreme right-wing party. We show that, with this method, we get a safer causal structure of variables (Bayesian Network) and make to identify a variable that satisfies the backdoor criterion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20network" title="Bayesian network">Bayesian network</a>, <a href="https://publications.waset.org/abstracts/search?q=structure%20learning" title=" structure learning"> structure learning</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20search" title=" optimal search"> optimal search</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=causal%20inference" title=" causal inference"> causal inference</a> </p> <a href="https://publications.waset.org/abstracts/151560/identification-of-bayesian-network-with-convolutional-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151560.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">176</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3514</span> Optimized Dynamic Bayesian Networks and Neural Verifier Test Applied to On-Line Isolated Characters Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Redouane%20Tlemsani">Redouane Tlemsani</a>, <a href="https://publications.waset.org/abstracts/search?q=Redouane"> Redouane</a>, <a href="https://publications.waset.org/abstracts/search?q=Belkacem%20Kouninef"> Belkacem Kouninef</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelkader%20Benyettou"> Abdelkader Benyettou </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, our system is a Markovien system which we can see it like a Dynamic Bayesian Networks. One of the major interests of these systems resides in the complete training of the models (topology and parameters) starting from training data. The Bayesian Networks are representing models of dubious knowledge on complex phenomena. They are a union between the theory of probability and the graph theory in order to give effective tools to represent a joined probability distribution on a set of random variables. The representation of knowledge bases on description, by graphs, relations of causality existing between the variables defining the field of study. The theory of Dynamic Bayesian Networks is a generalization of the Bayesians networks to the dynamic processes. Our objective amounts finding the better structure which represents the relationships (dependencies) between the variables of a dynamic bayesian network. In applications in pattern recognition, one will carry out the fixing of the structure which obliges us to admit some strong assumptions (for example independence between some variables). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arabic%20on%20line%20character%20recognition" title="Arabic on line character recognition">Arabic on line character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20Bayesian%20network" title=" dynamic Bayesian network"> dynamic Bayesian network</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=networks" title=" networks "> networks </a> </p> <a href="https://publications.waset.org/abstracts/34593/optimized-dynamic-bayesian-networks-and-neural-verifier-test-applied-to-on-line-isolated-characters-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34593.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">617</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3513</span> Inferential Reasoning for Heterogeneous Multi-Agent Mission</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sagir%20M.%20Yusuf">Sagir M. Yusuf</a>, <a href="https://publications.waset.org/abstracts/search?q=Chris%20Baber"> Chris Baber</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We describe issues bedeviling the coordination of heterogeneous (different sensors carrying agents) multi-agent missions such as belief conflict, situation reasoning, etc. We applied Bayesian and agents' presumptions inferential reasoning to solve the outlined issues with the heterogeneous multi-agent belief variation and situational-base reasoning. Bayesian Belief Network (BBN) was used in modeling the agents' belief conflict due to sensor variations. Simulation experiments were designed, and cases from agents’ missions were used in training the BBN using gradient descent and expectation-maximization algorithms. The output network is a well-trained BBN for making inferences for both agents and human experts. We claim that the Bayesian learning algorithm prediction capacity improves by the number of training data and argue that it enhances multi-agents robustness and solve agents’ sensor conflicts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20constraint%20optimization%20problem" title="distributed constraint optimization problem">distributed constraint optimization problem</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent%20system" title=" multi-agent system"> multi-agent system</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-robot%20coordination" title=" multi-robot coordination"> multi-robot coordination</a>, <a href="https://publications.waset.org/abstracts/search?q=autonomous%20system" title=" autonomous system"> autonomous system</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20intelligence" title=" swarm intelligence"> swarm intelligence</a> </p> <a href="https://publications.waset.org/abstracts/116896/inferential-reasoning-for-heterogeneous-multi-agent-mission" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116896.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3512</span> Fault Tree Analysis and Bayesian Network for Fire and Explosion of Crude Oil Tanks: Case Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Zerouali">B. Zerouali</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Kara"> M. Kara</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Hamaidi"> B. Hamaidi</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Mahdjoub"> H. Mahdjoub</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Rouabhia"> S. Rouabhia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a safety analysis for crude oil tanks to prevent undesirable events that may cause catastrophic accidents. The estimation of the probability of damage to industrial systems is carried out through a series of steps, and in accordance with a specific methodology. In this context, this work involves developing an assessment tool and risk analysis at the level of crude oil tanks system, based primarily on identification of various potential causes of crude oil tanks fire and explosion by the use of Fault Tree Analysis (FTA), then improved risk modelling by Bayesian Networks (BNs). Bayesian approach in the evaluation of failure and quantification of risks is a dynamic analysis approach. For this reason, have been selected as an analytical tool in this study. Research concludes that the Bayesian networks have a distinct and effective method in the safety analysis because of the flexibility of its structure; it is suitable for a wide variety of accident scenarios. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bayesian%20networks" title="bayesian networks">bayesian networks</a>, <a href="https://publications.waset.org/abstracts/search?q=crude%20oil%20tank" title=" crude oil tank"> crude oil tank</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20tree" title=" fault tree"> fault tree</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=safety" title=" safety"> safety</a> </p> <a href="https://publications.waset.org/abstracts/30636/fault-tree-analysis-and-bayesian-network-for-fire-and-explosion-of-crude-oil-tanks-case-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30636.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">660</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3511</span> Design and Development of High Strength Aluminium Alloy from Recycled 7xxx-Series Material Using Bayesian Optimisation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alireza%20Vahid">Alireza Vahid</a>, <a href="https://publications.waset.org/abstracts/search?q=Santu%20Rana"> Santu Rana</a>, <a href="https://publications.waset.org/abstracts/search?q=Sunil%20Gupta"> Sunil Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Pratibha%20Vellanki"> Pratibha Vellanki</a>, <a href="https://publications.waset.org/abstracts/search?q=Svetha%20Venkatesh"> Svetha Venkatesh</a>, <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Dorin"> Thomas Dorin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aluminum is the preferred material for lightweight applications and its alloys are constantly improving. The high strength 7xxx alloys have been extensively used for structural components in aerospace and automobile industries for the past 50 years. In the next decade, a great number of airplanes will be retired, providing an obvious source of valuable used metals and great demand for cost-effective methods to re-use these alloys. The design of proper aerospace alloys is primarily based on optimizing strength and ductility, both of which can be improved by controlling the additional alloying elements as well as heat treatment conditions. In this project, we explore the design of high-performance alloys with 7xxx as a base material. These designed alloys have to be optimized and improved to compare with modern 7xxx-series alloys and to remain competitive for aircraft manufacturing. Aerospace alloys are extremely complex with multiple alloying elements and numerous processing steps making optimization often intensive and costly. In the present study, we used Bayesian optimization algorithm, a well-known adaptive design strategy, to optimize this multi-variable system. An Al alloy was proposed and the relevant heat treatment schedules were optimized, using the tensile yield strength as the output to maximize. The designed alloy has a maximum yield strength and ultimate tensile strength of more than 730 and 760 MPa, respectively, and is thus comparable to the modern high strength 7xxx-series alloys. The microstructure of this alloy is characterized by electron microscopy, indicating that the increased strength of the alloy is due to the presence of a high number density of refined precipitates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aluminum%20alloys" title="aluminum alloys">aluminum alloys</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20optimization" title=" Bayesian optimization"> Bayesian optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=heat%20treatment" title=" heat treatment"> heat treatment</a>, <a href="https://publications.waset.org/abstracts/search?q=tensile%20properties" title=" tensile properties"> tensile properties</a> </p> <a href="https://publications.waset.org/abstracts/97215/design-and-development-of-high-strength-aluminium-alloy-from-recycled-7xxx-series-material-using-bayesian-optimisation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97215.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">119</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3510</span> Using Dynamic Bayesian Networks to Characterize and Predict Job Placement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xupin%20Zhang">Xupin Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Caterina%20Bramati"> Maria Caterina Bramati</a>, <a href="https://publications.waset.org/abstracts/search?q=Enrest%20Fokoue"> Enrest Fokoue</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Understanding the career placement of graduates from the university is crucial for both the qualities of education and ultimate satisfaction of students. In this research, we adapt the capabilities of dynamic Bayesian networks to characterize and predict students’ job placement using data from various universities. We also provide elements of the estimation of the indicator (score) of the strength of the network. The research focuses on overall findings as well as specific student groups including international and STEM students and their insight on the career path and what changes need to be made. The derived Bayesian network has the potential to be used as a tool for simulating the career path for students and ultimately helps universities in both academic advising and career counseling. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20bayesian%20networks" title="dynamic bayesian networks">dynamic bayesian networks</a>, <a href="https://publications.waset.org/abstracts/search?q=indicator%20estimation" title=" indicator estimation"> indicator estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=job%20placement" title=" job placement"> job placement</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20networks" title=" social networks"> social networks</a> </p> <a href="https://publications.waset.org/abstracts/61886/using-dynamic-bayesian-networks-to-characterize-and-predict-job-placement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61886.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">379</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3509</span> Estimating Occupancy in Residential Context Using Bayesian Networks for Energy Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manar%20Amayri">Manar Amayri</a>, <a href="https://publications.waset.org/abstracts/search?q=Hussain%20Kazimi"> Hussain Kazimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Quoc-Dung%20Ngo"> Quoc-Dung Ngo</a>, <a href="https://publications.waset.org/abstracts/search?q=Stephane%20Ploix"> Stephane Ploix</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A general approach is proposed to determine occupant behavior (occupancy and activity) in residential buildings and to use these estimates for improved energy management. Occupant behaviour is modelled with a Bayesian Network in an unsupervised manner. This algorithm makes use of domain knowledge gathered via questionnaires and recorded sensor data for motion detection, power, and hot water consumption as well as indoor CO₂ concentration. Two case studies are presented which show the real world applicability of estimating occupant behaviour in this way. Furthermore, experiments integrating occupancy estimation and hot water production control show that energy efficiency can be increased by roughly 5% over known optimal control techniques and more than 25% over rule-based control while maintaining the same occupant comfort standards. The efficiency gains are strongly correlated with occupant behaviour and accuracy of the occupancy estimates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy" title="energy">energy</a>, <a href="https://publications.waset.org/abstracts/search?q=management" title=" management"> management</a>, <a href="https://publications.waset.org/abstracts/search?q=control" title=" control"> control</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20methods" title=" Bayesian methods"> Bayesian methods</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20theory" title=" learning theory"> learning theory</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20networks" title=" sensor networks"> sensor networks</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20modelling%20and%20knowledge%20based%20systems" title=" knowledge modelling and knowledge based systems"> knowledge modelling and knowledge based systems</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=buildings" title=" buildings"> buildings</a> </p> <a href="https://publications.waset.org/abstracts/84739/estimating-occupancy-in-residential-context-using-bayesian-networks-for-energy-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84739.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">370</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3508</span> Human Action Recognition Using Variational Bayesian HMM with Dirichlet Process Mixture of Gaussian Wishart Emission Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wanhyun%20Cho">Wanhyun Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonja%20Kang"> Soonja Kang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sangkyoon%20Kim"> Sangkyoon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonyoung%20Park"> Soonyoung Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present the human action recognition method using the variational Bayesian HMM with the Dirichlet process mixture (DPM) of the Gaussian-Wishart emission model (GWEM). First, we define the Bayesian HMM based on the Dirichlet process, which allows an infinite number of Gaussian-Wishart components to support continuous emission observations. Second, we have considered an efficient variational Bayesian inference method that can be applied to drive the posterior distribution of hidden variables and model parameters for the proposed model based on training data. And then we have derived the predictive distribution that may be used to classify new action. Third, the paper proposes a process of extracting appropriate spatial-temporal feature vectors that can be used to recognize a wide range of human behaviors from input video image. Finally, we have conducted experiments that can evaluate the performance of the proposed method. The experimental results show that the method presented is more efficient with human action recognition than existing methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20action%20recognition" title="human action recognition">human action recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20HMM" title=" Bayesian HMM"> Bayesian HMM</a>, <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20process%20mixture%20model" title=" Dirichlet process mixture model"> Dirichlet process mixture model</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian-Wishart%20emission%20model" title=" Gaussian-Wishart emission model"> Gaussian-Wishart emission model</a>, <a href="https://publications.waset.org/abstracts/search?q=Variational%20Bayesian%20inference" title=" Variational Bayesian inference"> Variational Bayesian inference</a>, <a href="https://publications.waset.org/abstracts/search?q=prior%20distribution%20and%20approximate%20posterior%20distribution" title=" prior distribution and approximate posterior distribution"> prior distribution and approximate posterior distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=KTH%20dataset" title=" KTH dataset"> KTH dataset</a> </p> <a href="https://publications.waset.org/abstracts/49713/human-action-recognition-using-variational-bayesian-hmm-with-dirichlet-process-mixture-of-gaussian-wishart-emission-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49713.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">353</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3507</span> Financial Assets Return, Economic Factors and Investor's Behavioral Indicators Relationships Modeling: A Bayesian Networks Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nada%20Souissi">Nada Souissi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mourad%20Mroua"> Mourad Mroua</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main purpose of this study is to examine the interaction between financial asset volatility, economic factors and investor's behavioral indicators related to both the company's and the markets stocks for the period from January 2000 to January2020. Using multiple linear regression and Bayesian Networks modeling, results show a positive and negative relationship between investor's psychology index, economic factors and predicted stock market return. We reveal that the application of the Bayesian Discrete Network contributes to identify the different cause and effect relationships between all economic, financial variables and psychology index. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Financial%20asset%20return%20predictability" title="Financial asset return predictability">Financial asset return predictability</a>, <a href="https://publications.waset.org/abstracts/search?q=Economic%20factors" title=" Economic factors"> Economic factors</a>, <a href="https://publications.waset.org/abstracts/search?q=Investor%27s%20psychology%20index" title=" Investor's psychology index"> Investor's psychology index</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20approach" title=" Bayesian approach"> Bayesian approach</a>, <a href="https://publications.waset.org/abstracts/search?q=Probabilistic%20networks" title=" Probabilistic networks"> Probabilistic networks</a>, <a href="https://publications.waset.org/abstracts/search?q=Parametric%20learning" title=" Parametric learning"> Parametric learning</a> </p> <a href="https://publications.waset.org/abstracts/123056/financial-assets-return-economic-factors-and-investors-behavioral-indicators-relationships-modeling-a-bayesian-networks-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123056.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">149</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3506</span> Design of Bayesian MDS Sampling Plan Based on the Process Capability Index</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Davood%20Shishebori">Davood Shishebori</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Saber%20Fallah%20Nezhad"> Mohammad Saber Fallah Nezhad</a>, <a href="https://publications.waset.org/abstracts/search?q=Sina%20Seifi"> Sina Seifi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a variable multiple dependent state (MDS) sampling plan is developed based on the process capability index using Bayesian approach. The optimal parameters of the developed sampling plan with respect to constraints related to the risk of consumer and producer are presented. Two comparison studies have been done. First, the methods of double sampling model, sampling plan for resubmitted lots and repetitive group sampling (RGS) plan are elaborated and average sample numbers of the developed MDS plan and other classical methods are compared. A comparison study between the developed MDS plan based on Bayesian approach and the exact probability distribution is carried out. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MDS%20sampling%20plan" title="MDS sampling plan">MDS sampling plan</a>, <a href="https://publications.waset.org/abstracts/search?q=RGS%20plan" title=" RGS plan"> RGS plan</a>, <a href="https://publications.waset.org/abstracts/search?q=sampling%20plan%20for%20resubmitted%20lots" title=" sampling plan for resubmitted lots"> sampling plan for resubmitted lots</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20capability%20index%20%28PCI%29" title=" process capability index (PCI)"> process capability index (PCI)</a>, <a href="https://publications.waset.org/abstracts/search?q=average%20sample%20number%20%28ASN%29" title=" average sample number (ASN)"> average sample number (ASN)</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20approach" title=" Bayesian approach"> Bayesian approach</a> </p> <a href="https://publications.waset.org/abstracts/74571/design-of-bayesian-mds-sampling-plan-based-on-the-process-capability-index" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74571.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">301</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3505</span> A Two-Stage Bayesian Variable Selection Method with the Extension of Lasso for Geo-Referenced Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Georgiana%20Onicescu">Georgiana Onicescu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuqian%20Shen"> Yuqian Shen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the complex nature of geo-referenced data, multicollinearity of the risk factors in public health spatial studies is a commonly encountered issue, which leads to low parameter estimation accuracy because it inflates the variance in the regression analysis. To address this issue, we proposed a two-stage variable selection method by extending the least absolute shrinkage and selection operator (Lasso) to the Bayesian spatial setting, investigating the impact of risk factors to health outcomes. Specifically, in stage I, we performed the variable selection using Bayesian Lasso and several other variable selection approaches. Then, in stage II, we performed the model selection with only the selected variables from stage I and compared again the methods. To evaluate the performance of the two-stage variable selection methods, we conducted a simulation study with different distributions for the risk factors, using geo-referenced count data as the outcome and Michigan as the research region. We considered the cases when all candidate risk factors are independently normally distributed, or follow a multivariate normal distribution with different correlation levels. Two other Bayesian variable selection methods, Binary indicator, and the combination of Binary indicator and Lasso were considered and compared as alternative methods. The simulation results indicated that the proposed two-stage Bayesian Lasso variable selection method has the best performance for both independent and dependent cases considered. When compared with the one-stage approach, and the other two alternative methods, the two-stage Bayesian Lasso approach provides the highest estimation accuracy in all scenarios considered. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lasso" title="Lasso">Lasso</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20analysis" title=" Bayesian analysis"> Bayesian analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20analysis" title=" spatial analysis"> spatial analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20selection" title=" variable selection"> variable selection</a> </p> <a href="https://publications.waset.org/abstracts/105063/a-two-stage-bayesian-variable-selection-method-with-the-extension-of-lasso-for-geo-referenced-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105063.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">143</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3504</span> The Application of Bayesian Heuristic for Scheduling in Real-Time Private Clouds</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sahar%20Sohrabi">Sahar Sohrabi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The emergence of Cloud data centers has revolutionized the IT industry. Private Clouds in specific provide Cloud services for certain group of customers/businesses. In a real-time private Cloud each task that is given to the system has a deadline that desirably should not be violated. Scheduling tasks in a real-time private CLoud determine the way available resources in the system are shared among incoming tasks. The aim of the scheduling policy is to optimize the system outcome which for a real-time private Cloud can include: energy consumption, deadline violation, execution time and the number of host switches. Different scheduling policies can be used for scheduling. Each lead to a sub-optimal outcome in a certain settings of the system. A Bayesian Scheduling strategy is proposed for scheduling to further improve the system outcome. The Bayesian strategy showed to outperform all selected policies. It also has the flexibility in dealing with complex pattern of incoming task and has the ability to adapt. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title="cloud computing">cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling" title=" scheduling"> scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20private%20cloud" title=" real-time private cloud"> real-time private cloud</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian" title=" bayesian"> bayesian</a> </p> <a href="https://publications.waset.org/abstracts/38592/the-application-of-bayesian-heuristic-for-scheduling-in-real-time-private-clouds" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38592.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">359</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3503</span> Effect of Progressive Type-I Right Censoring on Bayesian Statistical Inference of Simple Step–Stress Acceleration Life Testing Plan under Weibull Life Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saleem%20Z.%20Ramadan">Saleem Z. Ramadan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses the effects of using progressive Type-I right censoring on the design of the Simple Step Accelerated Life testing using Bayesian approach for Weibull life products under the assumption of cumulative exposure model. The optimization criterion used in this paper is to minimize the expected pre-posterior variance of the PTH percentile time of failures. The model variables are the stress changing time and the stress value for the first step. A comparison between the conventional and the progressive Type-I right censoring is provided. The results have shown that the progressive Type-I right censoring reduces the cost of testing on the expense of the test precision when the sample size is small. Moreover, the results have shown that using strong priors or large sample size reduces the sensitivity of the test precision to the censoring proportion. Hence, the progressive Type-I right censoring is recommended in these cases as progressive Type-I right censoring reduces the cost of the test and doesn't affect the precision of the test a lot. Moreover, the results have shown that using direct or indirect priors affects the precision of the test. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reliability" title="reliability">reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=accelerated%20life%20testing" title=" accelerated life testing"> accelerated life testing</a>, <a href="https://publications.waset.org/abstracts/search?q=cumulative%20exposure%20model" title=" cumulative exposure model"> cumulative exposure model</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20estimation" title=" Bayesian estimation"> Bayesian estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=progressive%20type-I%20censoring" title=" progressive type-I censoring"> progressive type-I censoring</a>, <a href="https://publications.waset.org/abstracts/search?q=Weibull%20distribution" title=" Weibull distribution"> Weibull distribution</a> </p> <a href="https://publications.waset.org/abstracts/1761/effect-of-progressive-type-i-right-censoring-on-bayesian-statistical-inference-of-simple-step-stress-acceleration-life-testing-plan-under-weibull-life-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1761.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">504</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3502</span> Bayesian Meta-Analysis to Account for Heterogeneity in Studies Relating Life Events to Disease </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elizabeth%20Stojanovski">Elizabeth Stojanovski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Associations between life events and various forms of cancers have been identified. The purpose of a recent random-effects meta-analysis was to identify studies that examined the association between adverse events associated with changes to financial status including decreased income and breast cancer risk. The same association was studied in four separate studies which displayed traits that were not consistent between studies such as the study design, location and time frame. It was of interest to pool information from various studies to help identify characteristics that differentiated study results. Two random-effects Bayesian meta-analysis models are proposed to combine the reported estimates of the described studies. The proposed models allow major sources of variation to be taken into account, including study level characteristics, between study variance, and within study variance and illustrate the ease with which uncertainty can be incorporated using a hierarchical Bayesian modelling approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=random-effects" title="random-effects">random-effects</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-analysis" title=" meta-analysis"> meta-analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian" title=" Bayesian"> Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=variation" title=" variation"> variation</a> </p> <a href="https://publications.waset.org/abstracts/100263/bayesian-meta-analysis-to-account-for-heterogeneity-in-studies-relating-life-events-to-disease" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100263.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">160</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3501</span> Bayesian Analysis of Change Point Problems Using Conditionally Specified Priors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Golnaz%20Shahtahmassebi">Golnaz Shahtahmassebi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jose%20Maria%20Sarabia"> Jose Maria Sarabia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this talk, we introduce a new class of conjugate prior distributions obtained from conditional specification methodology. We illustrate the application of such distribution in Bayesian change point detection in Poisson processes. We obtain the posterior distribution of model parameters using a general bivariate distribution with gamma conditionals. Simulation from the posterior is readily implemented using a Gibbs sampling algorithm. The Gibbs sampling is implemented even when using conditional densities that are incompatible or only compatible with an improper joint density. The application of such methods will be demonstrated using examples of simulated and real data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=change%20point" title="change point">change point</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian%20inference" title=" bayesian inference"> bayesian inference</a>, <a href="https://publications.waset.org/abstracts/search?q=Gibbs%20sampler" title=" Gibbs sampler"> Gibbs sampler</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20specification" title=" conditional specification"> conditional specification</a>, <a href="https://publications.waset.org/abstracts/search?q=gamma%20conditional%20distributions" title=" gamma conditional distributions"> gamma conditional distributions</a> </p> <a href="https://publications.waset.org/abstracts/141782/bayesian-analysis-of-change-point-problems-using-conditionally-specified-priors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141782.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">189</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3500</span> Development of Terrorist Threat Prediction Model in Indonesia by Using Bayesian Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hilya%20Mudrika%20Arini">Hilya Mudrika Arini</a>, <a href="https://publications.waset.org/abstracts/search?q=Nur%20Aini%20Masruroh"> Nur Aini Masruroh</a>, <a href="https://publications.waset.org/abstracts/search?q=Budi%20Hartono"> Budi Hartono</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There are more than 20 terrorist threats from 2002 to 2012 in Indonesia. Despite of this fact, preventive solution through studies in the field of national security in Indonesia has not been conducted comprehensively. This study aims to provide a preventive solution by developing prediction model of the terrorist threat in Indonesia by using Bayesian network. There are eight stages to build the model, started from literature review, build and verify Bayesian belief network to what-if scenario. In order to build the model, four experts from different perspectives are utilized. This study finds several significant findings. First, news and the readiness of terrorist group are the most influent factor. Second, according to several scenarios of the news portion, it can be concluded that the higher positive news proportion, the higher probability of terrorist threat will occur. Therefore, the preventive solution to reduce the terrorist threat in Indonesia based on the model is by keeping the positive news portion to a maximum of 38%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20network" title="Bayesian network">Bayesian network</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20analysis" title=" decision analysis"> decision analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=national%20security%20system" title=" national security system"> national security system</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title=" text mining"> text mining</a> </p> <a href="https://publications.waset.org/abstracts/1453/development-of-terrorist-threat-prediction-model-in-indonesia-by-using-bayesian-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1453.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">392</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3499</span> Investigation of Compressive Strength of Fly Ash-Based Geopolymer Bricks with Hierarchical Bayesian Path Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ersin%20Sener">Ersin Sener</a>, <a href="https://publications.waset.org/abstracts/search?q=Ibrahim%20Demir"> Ibrahim Demir</a>, <a href="https://publications.waset.org/abstracts/search?q=Hasan%20Aykut%20Karaboga"> Hasan Aykut Karaboga</a>, <a href="https://publications.waset.org/abstracts/search?q=Kadir%20Kilinc"> Kadir Kilinc</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bayesian methods, which have very wide range of applications, are implemented to the data obtained from the production of F class fly ash-based geopolymer bricks’ experimental design. In this study, dependent variable is compressive strength, independent variables are treatment type (oven and steam), treatment time, molding time, temperature, water absorbtion ratio and density. The effect of independent variables on compressive strength is investigated. There is no difference among treatment types, but there is a correlation between independent variables. Therefore, hierarchical Bayesian path analysis is applied. In consequence of analysis we specified that treatment time, temperature and density effects on compressive strength is higher, molding time, and water absorbtion ratio is relatively low. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=experimental%20design" title="experimental design">experimental design</a>, <a href="https://publications.waset.org/abstracts/search?q=F%20class%20fly%20ash" title=" F class fly ash"> F class fly ash</a>, <a href="https://publications.waset.org/abstracts/search?q=geopolymer%20bricks" title=" geopolymer bricks"> geopolymer bricks</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20Bayesian%20path%20analysis" title=" hierarchical Bayesian path analysis"> hierarchical Bayesian path analysis</a> </p> <a href="https://publications.waset.org/abstracts/50683/investigation-of-compressive-strength-of-fly-ash-based-geopolymer-bricks-with-hierarchical-bayesian-path-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50683.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">387</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3498</span> Risk Factors for Defective Autoparts Products Using Bayesian Method in Poisson Generalized Linear Mixed Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pitsanu%20Tongkhow">Pitsanu Tongkhow</a>, <a href="https://publications.waset.org/abstracts/search?q=Pichet%20Jiraprasertwong"> Pichet Jiraprasertwong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research investigates risk factors for defective products in autoparts factories. Under a Bayesian framework, a generalized linear mixed model (GLMM) in which the dependent variable, the number of defective products, has a Poisson distribution is adopted. Its performance is compared with the Poisson GLM under a Bayesian framework. The factors considered are production process, machines, and workers. The products coded RT50 are observed. The study found that the Poisson GLMM is more appropriate than the Poisson GLM. For the production Process factor, the highest risk of producing defective products is Process 1, for the Machine factor, the highest risk is Machine 5, and for the Worker factor, the highest risk is Worker 6. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=defective%20autoparts%20products" title="defective autoparts products">defective autoparts products</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20framework" title=" Bayesian framework"> Bayesian framework</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20linear%20mixed%20model%20%28GLMM%29" title=" generalized linear mixed model (GLMM)"> generalized linear mixed model (GLMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20factors" title=" risk factors "> risk factors </a> </p> <a href="https://publications.waset.org/abstracts/10195/risk-factors-for-defective-autoparts-products-using-bayesian-method-in-poisson-generalized-linear-mixed-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10195.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">569</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3497</span> Supplier Risk Management: A Multivariate Statistical Modelling and Portfolio Optimization Based Approach for Supplier Delivery Performance Development </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiahui%20Yang">Jiahui Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=John%20Quigley"> John Quigley</a>, <a href="https://publications.waset.org/abstracts/search?q=Lesley%20Walls"> Lesley Walls</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the authors develop a stochastic model regarding the investment in supplier delivery performance development from a buyer’s perspective. The authors propose a multivariate model through a Multinomial-Dirichlet distribution within an Empirical Bayesian inference framework, representing both the epistemic and aleatory uncertainties in deliveries. A closed form solution is obtained and the lower and upper bound for both optimal investment level and expected profit under uncertainty are derived. The theoretical properties provide decision makers with useful insights regarding supplier delivery performance improvement problems where multiple delivery statuses are involved. The authors also extend the model from a single supplier investment into a supplier portfolio, using a Lagrangian method to obtain a theoretical expression for an optimal investment level and overall expected profit. The model enables a buyer to know how the marginal expected profit/investment level of each supplier changes with respect to the budget and which supplier should be invested in when additional budget is available. An application of this model is illustrated in a simulation study. Overall, the main contribution of this study is to provide an optimal investment decision making framework for supplier development, taking into account multiple delivery statuses as well as multiple projects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title="decision making">decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20bayesian" title=" empirical bayesian"> empirical bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=supplier%20development" title=" supplier development"> supplier development</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20management" title=" supply chain management"> supply chain management</a> </p> <a href="https://publications.waset.org/abstracts/89731/supplier-risk-management-a-multivariate-statistical-modelling-and-portfolio-optimization-based-approach-for-supplier-delivery-performance-development" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89731.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">288</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=bayesian%20optimization&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=bayesian%20optimization&page=3">3</a></li> <li class="page-item"><a class="page-link" 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