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models</title> <meta name="description" content="Search results for: stochastic models"> <meta name="keywords" content="stochastic models"> <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 d-lg-none 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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="stochastic models"> <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> 7080</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: stochastic models</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7080</span> Stochastic Age-Structured Population Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arcady%20Ponosov">Arcady Ponosov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many well-known age-structured population models are derived from the celebrated McKendrick-von Foerster equation (MFE), also called the biological conservation law. A similar technique is suggested for the stochastically perturbed MFE. This technique is shown to produce stochastic versions of the deterministic population models, which appear to be very different from those one can construct by simply appending additive stochasticity to deterministic equations. In particular, it is shown that stochastic Nicholson’s blowflies model should contain both additive and multiplicative stochastic noises. The suggested transformation technique is similar to that used in the deterministic case. The difference is hidden in the formulas for the exact solutions of the simplified boundary value problem for the stochastically perturbed MFE. The analysis is also based on the theory of stochastic delay differential equations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=boundary%20value%20problems" title="boundary value problems">boundary value problems</a>, <a href="https://publications.waset.org/abstracts/search?q=population%20models" title=" population models"> population models</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20delay%20differential%20equations" title=" stochastic delay differential equations"> stochastic delay differential equations</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20partial%20differential%20equation" title=" stochastic partial differential equation"> stochastic partial differential equation</a> </p> <a href="https://publications.waset.org/abstracts/138398/stochastic-age-structured-population-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138398.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">254</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">7079</span> Modelling High-Frequency Crude Oil Dynamics Using Affine and Non-Affine Jump-Diffusion Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Katja%20Ignatieva">Katja Ignatieva</a>, <a href="https://publications.waset.org/abstracts/search?q=Patrick%20Wong"> Patrick Wong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We investigated the dynamics of high frequency energy prices, including crude oil and electricity prices. The returns of underlying quantities are modelled using various parametric models such as stochastic framework with jumps and stochastic volatility (SVCJ) as well as non-parametric alternatives, which are purely data driven and do not require specification of the drift or the diffusion coefficient function. Using different statistical criteria, we investigate the performance of considered parametric and nonparametric models in their ability to forecast price series and volatilities. Our models incorporate possible seasonalities in the underlying dynamics and utilise advanced estimation techniques for the dynamics of energy prices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stochastic%20volatility" title="stochastic volatility">stochastic volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=affine%20jump-diffusion%20models" title=" affine jump-diffusion models"> affine jump-diffusion models</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20frequency%20data" title=" high frequency data"> high frequency data</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20specification" title=" model specification"> model specification</a>, <a href="https://publications.waset.org/abstracts/search?q=markov%20chain%20monte%20carlo" title=" markov chain monte carlo"> markov chain monte carlo</a> </p> <a href="https://publications.waset.org/abstracts/159124/modelling-high-frequency-crude-oil-dynamics-using-affine-and-non-affine-jump-diffusion-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159124.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">104</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">7078</span> Hybrid Equity Warrants Pricing Formulation under Stochastic Dynamics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Teh%20Raihana%20Nazirah%20Roslan">Teh Raihana Nazirah Roslan</a>, <a href="https://publications.waset.org/abstracts/search?q=Siti%20Zulaiha%20Ibrahim"> Siti Zulaiha Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Sharmila%20Karim"> Sharmila Karim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A warrant is a financial contract that confers the right but not the obligation, to buy or sell a security at a certain price before expiration. The standard procedure to value equity warrants using call option pricing models such as the Black–Scholes model had been proven to contain many flaws, such as the assumption of constant interest rate and constant volatility. In fact, existing alternative models were found focusing more on demonstrating techniques for pricing, rather than empirical testing. Therefore, a mathematical model for pricing and analyzing equity warrants which comprises stochastic interest rate and stochastic volatility is essential to incorporate the dynamic relationships between the identified variables and illustrate the real market. Here, the aim is to develop dynamic pricing formulations for hybrid equity warrants by incorporating stochastic interest rates from the Cox-Ingersoll-Ross (CIR) model, along with stochastic volatility from the Heston model. The development of the model involves the derivations of stochastic differential equations that govern the model dynamics. The resulting equations which involve Cauchy problem and heat equations are then solved using partial differential equation approaches. The analytical pricing formulas obtained in this study comply with the form of analytical expressions embedded in the Black-Scholes model and other existing pricing models for equity warrants. This facilitates the practicality of this proposed formula for comparison purposes and further empirical study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cox-Ingersoll-Ross%20model" title="Cox-Ingersoll-Ross model">Cox-Ingersoll-Ross model</a>, <a href="https://publications.waset.org/abstracts/search?q=equity%20warrants" title=" equity warrants"> equity warrants</a>, <a href="https://publications.waset.org/abstracts/search?q=Heston%20model" title=" Heston model"> Heston model</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20models" title=" hybrid models"> hybrid models</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic" title=" stochastic"> stochastic</a> </p> <a href="https://publications.waset.org/abstracts/124157/hybrid-equity-warrants-pricing-formulation-under-stochastic-dynamics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124157.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">129</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">7077</span> The Use of Stochastic Gradient Boosting Method for Multi-Model Combination of Rainfall-Runoff Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Phanida%20Phukoetphim">Phanida Phukoetphim</a>, <a href="https://publications.waset.org/abstracts/search?q=Asaad%20Y.%20Shamseldin"> Asaad Y. Shamseldin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, the novel Stochastic Gradient Boosting (SGB) combination method is addressed for producing daily river flows from four different rain-runoff models of Ohinemuri catchment, New Zealand. The selected rainfall-runoff models are two empirical black-box models: linear perturbation model and linear varying gain factor model, two conceptual models: soil moisture accounting and routing model and Nedbør-Afrstrømnings model. In this study, the simple average combination method and the weighted average combination method were used as a benchmark for comparing the results of the novel SGB combination method. The models and combination results are evaluated using statistical and graphical criteria. Overall results of this study show that the use of combination technique can certainly improve the simulated river flows of four selected models for Ohinemuri catchment, New Zealand. The results also indicate that the novel SGB combination method is capable of accurate prediction when used in a combination method of the simulated river flows in New Zealand. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-model%20combination" title="multi-model combination">multi-model combination</a>, <a href="https://publications.waset.org/abstracts/search?q=rainfall-runoff%20modeling" title=" rainfall-runoff modeling"> rainfall-runoff modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20gradient%20boosting" title=" stochastic gradient boosting"> stochastic gradient boosting</a>, <a href="https://publications.waset.org/abstracts/search?q=bioinformatics" title=" bioinformatics"> bioinformatics</a> </p> <a href="https://publications.waset.org/abstracts/3455/the-use-of-stochastic-gradient-boosting-method-for-multi-model-combination-of-rainfall-runoff-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3455.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">339</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">7076</span> On Stochastic Models for Fine-Scale Rainfall Based on Doubly Stochastic Poisson Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nadarajah%20I.%20Ramesh">Nadarajah I. Ramesh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Much of the research on stochastic point process models for rainfall has focused on Poisson cluster models constructed from either the Neyman-Scott or Bartlett-Lewis processes. The doubly stochastic Poisson process provides a rich class of point process models, especially for fine-scale rainfall modelling. This paper provides an account of recent development on this topic and presents the results based on some of the fine-scale rainfall models constructed from this class of stochastic point processes. Amongst the literature on stochastic models for rainfall, greater emphasis has been placed on modelling rainfall data recorded at hourly or daily aggregation levels. Stochastic models for sub-hourly rainfall are equally important, as there is a need to reproduce rainfall time series at fine temporal resolutions in some hydrological applications. For example, the study of climate change impacts on hydrology and water management initiatives requires the availability of data at fine temporal resolutions. One approach to generating such rainfall data relies on the combination of an hourly stochastic rainfall simulator, together with a disaggregator making use of downscaling techniques. Recent work on this topic adopted a different approach by developing specialist stochastic point process models for fine-scale rainfall aimed at generating synthetic precipitation time series directly from the proposed stochastic model. One strand of this approach focused on developing a class of doubly stochastic Poisson process (DSPP) models for fine-scale rainfall to analyse data collected in the form of rainfall bucket tip time series. In this context, the arrival pattern of rain gauge bucket tip times N(t) is viewed as a DSPP whose rate of occurrence varies according to an unobserved finite state irreducible Markov process X(t). Since the likelihood function of this process can be obtained, by conditioning on the underlying Markov process X(t), the models were fitted with maximum likelihood methods. The proposed models were applied directly to the raw data collected by tipping-bucket rain gauges, thus avoiding the need to convert tip-times to rainfall depths prior to fitting the models. One advantage of this approach was that the use of maximum likelihood methods enables a more straightforward estimation of parameter uncertainty and comparison of sub-models of interest. Another strand of this approach employed the DSPP model for the arrivals of rain cells and attached a pulse or a cluster of pulses to each rain cell. Different mechanisms for the pattern of the pulse process were used to construct variants of this model. We present the results of these models when they were fitted to hourly and sub-hourly rainfall data. The results of our analysis suggest that the proposed class of stochastic models is capable of reproducing the fine-scale structure of the rainfall process, and hence provides a useful tool in hydrological modelling. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fine-scale%20rainfall" title="fine-scale rainfall">fine-scale rainfall</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood" title=" maximum likelihood"> maximum likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=point%20process" title=" point process"> point process</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20model" title=" stochastic model"> stochastic model</a> </p> <a href="https://publications.waset.org/abstracts/90388/on-stochastic-models-for-fine-scale-rainfall-based-on-doubly-stochastic-poisson-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90388.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">278</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">7075</span> Comparative Study and Parallel Implementation of Stochastic Models for Pricing of European Options Portfolios using Monte Carlo Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vinayak%20Bassi">Vinayak Bassi</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajpreet%20Singh"> Rajpreet Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over the years, with the emergence of sophisticated computers and algorithms, finance has been quantified using computational prowess. Asset valuation has been one of the key components of quantitative finance. In fact, it has become one of the embryonic steps in determining risk related to a portfolio, the main goal of quantitative finance. This study comprises a drawing comparison between valuation output generated by two stochastic dynamic models, namely Black-Scholes and Dupire’s bi-dimensionality model. Both of these models are formulated for computing the valuation function for a portfolio of European options using Monte Carlo simulation methods. Although Monte Carlo algorithms have a slower convergence rate than calculus-based simulation techniques (like FDM), they work quite effectively over high-dimensional dynamic models. A fidelity gap is analyzed between the static (historical) and stochastic inputs for a sample portfolio of underlying assets. In order to enhance the performance efficiency of the model, the study emphasized the use of variable reduction methods and customizing random number generators to implement parallelization. An attempt has been made to further implement the Dupire’s model on a GPU to achieve higher computational performance. Furthermore, ideas have been discussed around the performance enhancement and bottleneck identification related to the implementation of options-pricing models on GPUs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=monte%20carlo" title="monte carlo">monte carlo</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20models" title=" stochastic models"> stochastic models</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20finance" title=" computational finance"> computational finance</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20programming" title=" parallel programming"> parallel programming</a>, <a href="https://publications.waset.org/abstracts/search?q=scientific%20computing" title=" scientific computing"> scientific computing</a> </p> <a href="https://publications.waset.org/abstracts/143605/comparative-study-and-parallel-implementation-of-stochastic-models-for-pricing-of-european-options-portfolios-using-monte-carlo-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143605.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">161</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">7074</span> Regularization of Gene Regulatory Networks Perturbed by White Noise</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramazan%20I.%20Kadiev">Ramazan I. Kadiev</a>, <a href="https://publications.waset.org/abstracts/search?q=Arcady%20Ponosov"> Arcady Ponosov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mathematical models of gene regulatory networks can in many cases be described by ordinary differential equations with switching nonlinearities, where the initial value problem is ill-posed. Several regularization methods are known in the case of deterministic networks, but the presence of stochastic noise leads to several technical difficulties. In the presentation, it is proposed to apply the methods of the stochastic singular perturbation theory going back to Yu. Kabanov and Yu. Pergamentshchikov. This approach is used to regularize the above ill-posed problem, which, e.g., makes it possible to design stable numerical schemes. Several examples are provided in the presentation, which support the efficiency of the suggested analysis. The method can also be of interest in other fields of biomathematics, where differential equations contain switchings, e.g., in neural field models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ill-posed%20problems" title="ill-posed problems">ill-posed problems</a>, <a href="https://publications.waset.org/abstracts/search?q=singular%20perturbation%20analysis" title=" singular perturbation analysis"> singular perturbation analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20differential%20equations" title=" stochastic differential equations"> stochastic differential equations</a>, <a href="https://publications.waset.org/abstracts/search?q=switching%20nonlinearities" title=" switching nonlinearities"> switching nonlinearities</a> </p> <a href="https://publications.waset.org/abstracts/85883/regularization-of-gene-regulatory-networks-perturbed-by-white-noise" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85883.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">194</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">7073</span> Stochastic Programming and C-Somga: Animal Ration Formulation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pratiksha%20Saxena">Pratiksha Saxena</a>, <a href="https://publications.waset.org/abstracts/search?q=Dipti%20Singh"> Dipti Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Neha%20Khanna"> Neha Khanna</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A self-organizing migrating genetic algorithm(C-SOMGA) is developed for animal diet formulation. This paper presents animal diet formulation using stochastic and genetic algorithm. Tri-objective models for cost minimization and shelf life maximization are developed. These objectives are achieved by combination of stochastic programming and C-SOMGA. Stochastic programming is used to introduce nutrient variability for animal diet. Self-organizing migrating genetic algorithm provides exact and quick solution and presents an innovative approach towards successful application of soft computing technique in the area of animal diet formulation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=animal%20feed%20ration" title="animal feed ration">animal feed ration</a>, <a href="https://publications.waset.org/abstracts/search?q=feed%20formulation" title=" feed formulation"> feed formulation</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20programming" title=" linear programming"> linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20programming" title=" stochastic programming"> stochastic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=self-migrating%20genetic%20algorithm" title=" self-migrating genetic algorithm"> self-migrating genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=C-SOMGA%20technique" title=" C-SOMGA technique"> C-SOMGA technique</a>, <a href="https://publications.waset.org/abstracts/search?q=shelf%20life%20maximization" title=" shelf life maximization"> shelf life maximization</a>, <a href="https://publications.waset.org/abstracts/search?q=cost%20minimization" title=" cost minimization"> cost minimization</a>, <a href="https://publications.waset.org/abstracts/search?q=nutrient%20maximization" title=" nutrient maximization"> nutrient maximization</a> </p> <a href="https://publications.waset.org/abstracts/35795/stochastic-programming-and-c-somga-animal-ration-formulation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35795.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">442</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">7072</span> Evaluating the Effects of a Positive Bitcoin Shock on the U.S Economy: A TVP-FAVAR Model with Stochastic Volatility</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Olfa%20Kaabia">Olfa Kaabia</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilyes%20Abid"> Ilyes Abid</a>, <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Guesmi"> Khaled Guesmi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This pioneer paper studies whether and how Bitcoin shocks are transmitted to the U.S economy. We employ a new methodology: TVP FAVAR model with stochastic volatility. We use a large dataset of 111 major U.S variables from 1959:m1 to 2016:m12. The results show that Bitcoin shocks significantly impact the U.S. economy. This significant impact is pronounced in a volatile and increasing U.S economy. The Bitcoin has a positive relationship on the U.S real activity, and a negative one on U.S prices and interest rates. Effects on the Monetary Policy exist via the inter-est rates and the Money, Credit and Finance transmission channels. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bitcoin" title="bitcoin">bitcoin</a>, <a href="https://publications.waset.org/abstracts/search?q=US%20economy" title=" US economy"> US economy</a>, <a href="https://publications.waset.org/abstracts/search?q=FAVAR%20models" title=" FAVAR models"> FAVAR models</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20volatility" title=" stochastic volatility"> stochastic volatility</a> </p> <a href="https://publications.waset.org/abstracts/78168/evaluating-the-effects-of-a-positive-bitcoin-shock-on-the-us-economy-a-tvp-favar-model-with-stochastic-volatility" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78168.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">247</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">7071</span> CE Method for Development of Japan's Stochastic Earthquake Catalogue</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Babak%20Kamrani">Babak Kamrani</a>, <a href="https://publications.waset.org/abstracts/search?q=Nozar%20Kishi"> Nozar Kishi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Stochastic catalog represents the events module of the earthquake loss estimation models. It includes series of events with different magnitudes and corresponding frequencies/probabilities. For the development of the stochastic catalog, random or uniform sampling methods are used to sample the events from the seismicity model. For covering all the Magnitude Frequency Distribution (MFD), a huge number of events should be generated for the above-mentioned methods. Characteristic Event (CE) method chooses the events based on the interest of the insurance industry. We divide the MFD of each source into bins. We have chosen the bins based on the probability of the interest by the insurance industry. First, we have collected the information for the available seismic sources. Sources are divided into Fault sources, subduction, and events without specific fault source. We have developed the MFD for each of the individual and areal source based on the seismicity of the sources. Afterward, we have calculated the CE magnitudes based on the desired probability. To develop the stochastic catalog, we have introduced uncertainty to the location of the events too. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stochastic%20catalogue" title="stochastic catalogue">stochastic catalogue</a>, <a href="https://publications.waset.org/abstracts/search?q=earthquake%20loss" title=" earthquake loss"> earthquake loss</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=characteristic%20event" title=" characteristic event"> characteristic event</a> </p> <a href="https://publications.waset.org/abstracts/63618/ce-method-for-development-of-japans-stochastic-earthquake-catalogue" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63618.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">298</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">7070</span> On Differential Growth Equation to Stochastic Growth Model Using Hyperbolic Sine Function in Height/Diameter Modeling of Pines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20O.%20Oyamakin">S. O. Oyamakin</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20U.%20Chukwu"> A. U. Chukwu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Richard's growth equation being a generalized logistic growth equation was improved upon by introducing an allometric parameter using the hyperbolic sine function. The integral solution to this was called hyperbolic Richard's growth model having transformed the solution from deterministic to a stochastic growth model. Its ability in model prediction was compared with the classical Richard's growth model an approach which mimicked the natural variability of heights/diameter increment with respect to age and therefore provides a more realistic height/diameter predictions using the coefficient of determination (R2), Mean Absolute Error (MAE) and Mean Square Error (MSE) results. The Kolmogorov-Smirnov test and Shapiro-Wilk test was also used to test the behavior of the error term for possible violations. The mean function of top height/Dbh over age using the two models under study predicted closely the observed values of top height/Dbh in the hyperbolic Richard's nonlinear growth models better than the classical Richard's growth model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=height" title="height">height</a>, <a href="https://publications.waset.org/abstracts/search?q=Dbh" title=" Dbh"> Dbh</a>, <a href="https://publications.waset.org/abstracts/search?q=forest" title=" forest"> forest</a>, <a href="https://publications.waset.org/abstracts/search?q=Pinus%20caribaea" title=" Pinus caribaea"> Pinus caribaea</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperbolic" title=" hyperbolic"> hyperbolic</a>, <a href="https://publications.waset.org/abstracts/search?q=Richard%27s" title=" Richard's"> Richard's</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic" title=" stochastic"> stochastic</a> </p> <a href="https://publications.waset.org/abstracts/17738/on-differential-growth-equation-to-stochastic-growth-model-using-hyperbolic-sine-function-in-heightdiameter-modeling-of-pines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17738.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">480</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">7069</span> Non-Stationary Stochastic Optimization of an Oscillating Water Column</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mar%C3%ADa%20L.%20Jal%C3%B3n">María L. Jalón</a>, <a href="https://publications.waset.org/abstracts/search?q=Feargal%20Brennan"> Feargal Brennan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A non-stationary stochastic optimization methodology is applied to an OWC (oscillating water column) to find the design that maximizes the wave energy extraction. Different temporal cycles are considered to represent the long-term variability of the wave climate at the site in the optimization problem. The results of the non-stationary stochastic optimization problem are compared against those obtained by a stationary stochastic optimization problem. The comparative analysis reveals that the proposed non-stationary optimization provides designs with a better fit to reality. However, the stationarity assumption can be adequate when looking at averaged system response. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=non-stationary%20stochastic%20optimization" title="non-stationary stochastic optimization">non-stationary stochastic optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=oscillating%20water" title=" oscillating water"> oscillating water</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20variability" title=" temporal variability"> temporal variability</a>, <a href="https://publications.waset.org/abstracts/search?q=wave%20energy" title=" wave energy"> wave energy</a> </p> <a href="https://publications.waset.org/abstracts/75300/non-stationary-stochastic-optimization-of-an-oscillating-water-column" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75300.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">373</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">7068</span> Wind Power Forecast Error Simulation Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Josip%20Vasilj">Josip Vasilj</a>, <a href="https://publications.waset.org/abstracts/search?q=Petar%20Sarajcev"> Petar Sarajcev</a>, <a href="https://publications.waset.org/abstracts/search?q=Damir%20Jakus"> Damir Jakus</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the major difficulties introduced with wind power penetration is the inherent uncertainty in production originating from uncertain wind conditions. This uncertainty impacts many different aspects of power system operation, especially the balancing power requirements. For this reason, in power system development planing, it is necessary to evaluate the potential uncertainty in future wind power generation. For this purpose, simulation models are required, reproducing the performance of wind power forecasts. This paper presents a wind power forecast error simulation models which are based on the stochastic process simulation. Proposed models capture the most important statistical parameters recognized in wind power forecast error time series. Furthermore, two distinct models are presented based on data availability. First model uses wind speed measurements on potential or existing wind power plant locations, while the seconds model uses statistical distribution of wind speeds. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wind%20power" title="wind power">wind power</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20process" title=" stochastic process"> stochastic process</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20simulation" title=" Monte Carlo simulation"> Monte Carlo simulation</a> </p> <a href="https://publications.waset.org/abstracts/17977/wind-power-forecast-error-simulation-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17977.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">483</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">7067</span> Weak Solutions Of Stochastic Fractional Differential Equations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lev%20Idels">Lev Idels</a>, <a href="https://publications.waset.org/abstracts/search?q=Arcady%20Ponosov"> Arcady Ponosov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Stochastic fractional differential equations have recently attracted considerable attention, as they have been used to model real-world processes, which are subject to natural memory effects and measurement uncertainties. Compared to conventional hereditary differential equations, one of the advantages of fractional differential equations is related to more realistic geometric properties of their trajectories that do not intersect in the phase space. In this report, a Peano-like existence theorem for nonlinear stochastic fractional differential equations is proven under very general hypotheses. Several specific classes of equations are checked to satisfy these hypotheses, including delay equations driven by the fractional Brownian motion, stochastic fractional neutral equations and many others. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=delay%20equations" title="delay equations">delay equations</a>, <a href="https://publications.waset.org/abstracts/search?q=operator%20methods" title=" operator methods"> operator methods</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20noise" title=" stochastic noise"> stochastic noise</a>, <a href="https://publications.waset.org/abstracts/search?q=weak%20solutions" title=" weak solutions"> weak solutions</a> </p> <a href="https://publications.waset.org/abstracts/146592/weak-solutions-of-stochastic-fractional-differential-equations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146592.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">209</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">7066</span> Performance and Availability Analysis of 2N Redundancy Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yutae%20Lee">Yutae Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we consider the performance and availability of a redundancy model. The redundancy model is a form of resilience that ensures service availability in the event of component failure. This paper considers a 2N redundancy model. In the model there are at most one active service unit and at most one standby service unit. The active one is providing the service while the standby is prepared to take over the active role when the active fails. We design our analysis model using Stochastic Reward Nets, and then evaluate the performance and availability of 2N redundancy model using Stochastic Petri Net Package (SPNP). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=availability" title="availability">availability</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20reward%20net" title=" stochastic reward net"> stochastic reward net</a>, <a href="https://publications.waset.org/abstracts/search?q=2N%20redundancy" title=" 2N redundancy"> 2N redundancy</a> </p> <a href="https://publications.waset.org/abstracts/40741/performance-and-availability-analysis-of-2n-redundancy-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40741.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">420</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">7065</span> Lyapunov and Input-to-State Stability of Stochastic Differential Equations </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arcady%20Ponosov">Arcady Ponosov</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramazan%20Kadiev"> Ramazan Kadiev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Input-to-State Stability (ISS) is widely used in deterministic control theory but less known in the stochastic case. Roughly speaking, the theory explains when small perturbations of the right-hand sides of the system on the entire semiaxis cause only small changes in the solutions of the system, again on the entire semiaxis. This property is crucial in many applications. In the report, we explain how to define and study ISS for systems of linear stochastic differential equations with or without delays. The central result connects ISS with the property of Lyapunov stability. This relationship is well-known in the deterministic setting, but its stochastic version is new. As an application, a method of studying asymptotic Lyapunov stability for stochastic delay equations is described and justified. Several examples are provided that confirm the efficiency and simplicity of the framework. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asymptotic%20stability" title="asymptotic stability">asymptotic stability</a>, <a href="https://publications.waset.org/abstracts/search?q=delay%20equations" title=" delay equations"> delay equations</a>, <a href="https://publications.waset.org/abstracts/search?q=operator%20methods" title=" operator methods"> operator methods</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20perturbations" title=" stochastic perturbations"> stochastic perturbations</a> </p> <a href="https://publications.waset.org/abstracts/127764/lyapunov-and-input-to-state-stability-of-stochastic-differential-equations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127764.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">175</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">7064</span> Modelling Structural Breaks in Stock Price Time Series Using Stochastic Differential Equations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daniil%20Karzanov">Daniil Karzanov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper studies the effect of quarterly earnings reports on the stock price. The profitability of the stock is modeled by geometric Brownian diffusion and the Constant Elasticity of Variance model. We fit several variations of stochastic differential equations to the pre-and after-report period using the Maximum Likelihood Estimation and Grid Search of parameters method. By examining the change in the model parameters after reports’ publication, the study reveals that the reports have enough evidence to be a structural breakpoint, meaning that all the forecast models exploited are not applicable for forecasting and should be refitted shortly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stock%20market" title="stock market">stock market</a>, <a href="https://publications.waset.org/abstracts/search?q=earnings%20reports" title=" earnings reports"> earnings reports</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20time%20series" title=" financial time series"> financial time series</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20breaks" title=" structural breaks"> structural breaks</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20differential%20equations" title=" stochastic differential equations"> stochastic differential equations</a> </p> <a href="https://publications.waset.org/abstracts/143167/modelling-structural-breaks-in-stock-price-time-series-using-stochastic-differential-equations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143167.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">205</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">7063</span> Analysis of Financial Time Series by Using Ornstein-Uhlenbeck Type Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Md%20Al%20Masum%20Bhuiyan">Md Al Masum Bhuiyan</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20C.%20Mariani"> Maria C. Mariani</a>, <a href="https://publications.waset.org/abstracts/search?q=Osei%20K.%20Tweneboah"> Osei K. Tweneboah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the present work, we develop a technique for estimating the volatility of financial time series by using stochastic differential equation. Taking the daily closing prices from developed and emergent stock markets as the basis, we argue that the incorporation of stochastic volatility into the time-varying parameter estimation significantly improves the forecasting performance via Maximum Likelihood Estimation. While using the technique, we see the long-memory behavior of data sets and one-step-ahead-predicted log-volatility with ±2 standard errors despite the variation of the observed noise from a Normal mixture distribution, because the financial data studied is not fully Gaussian. Also, the Ornstein-Uhlenbeck process followed in this work simulates well the financial time series, which aligns our estimation algorithm with large data sets due to the fact that this algorithm has good convergence properties. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=financial%20time%20series" title="financial time series">financial time series</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Ornstein-Uhlenbeck%20type%20models" title=" Ornstein-Uhlenbeck type models"> Ornstein-Uhlenbeck type models</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20volatility%20model" title=" stochastic volatility model"> stochastic volatility model</a> </p> <a href="https://publications.waset.org/abstracts/73856/analysis-of-financial-time-series-by-using-ornstein-uhlenbeck-type-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73856.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">241</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">7062</span> Stability of Solutions of Semidiscrete Stochastic Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramazan%20Kadiev">Ramazan Kadiev</a>, <a href="https://publications.waset.org/abstracts/search?q=Arkadi%20Ponossov"> Arkadi Ponossov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Semidiscrete systems contain both continuous and discrete components. This means that the dynamics is mostly continuous, but at certain instants, it is exposed to abrupt influences. Such systems naturally appear in applications, for example, in biological and ecological models as well as in the control theory. Therefore, the study of semidiscrete systems has recently attracted the attention of many specialists. Stochastic effects are an important part of any realistic approach to modeling. For example, stochasticity arises in the population dynamics, demographic and ecological due to a change in time of factors external to the system affecting the survival of the population. In control theory, random coefficients can simulate inaccuracies in measurements. It will be shown in the presentation how to incorporate such effects into semidiscrete systems. Stability analysis is an essential part of modeling real-world problems. In the presentation, it will be explained how sufficient conditions for the moment stability of solutions in terms of the coefficients for linear semidiscrete stochastic equations can be derived using non-Lyapunov technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=abrupt%20changes" title="abrupt changes">abrupt changes</a>, <a href="https://publications.waset.org/abstracts/search?q=exponential%20stability" title=" exponential stability"> exponential stability</a>, <a href="https://publications.waset.org/abstracts/search?q=regularization" title=" regularization"> regularization</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20noises" title=" stochastic noises"> stochastic noises</a> </p> <a href="https://publications.waset.org/abstracts/144470/stability-of-solutions-of-semidiscrete-stochastic-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144470.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">187</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">7061</span> Estimation of Probabilistic Fatigue Crack Propagation Models of AZ31 Magnesium Alloys under Various Load Ratio Conditions by Using the Interpolation of a Random Variable</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seon%20Soon%20Choi">Seon Soon Choi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The essential purpose is to present the good fatigue crack propagation model describing a stochastic fatigue crack growth behavior in a rolled magnesium alloy, AZ31, under various load ratio conditions. Fatigue crack propagation experiments were carried out in laboratory air under four conditions of load ratio, R, using AZ31 to investigate the crack growth behavior. The stochastic fatigue crack growth behavior was analyzed using an interpolation of random variable, Z, introduced to an empirical fatigue crack propagation model. The empirical fatigue models used in this study are Paris-Erdogan model, Walker model, Forman model, and modified Forman model. It was found that the random variable is useful in describing the stochastic fatigue crack growth behaviors under various load ratio conditions. The good probabilistic model describing a stochastic fatigue crack growth behavior under various load ratio conditions was also proposed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=magnesium%20alloys" title="magnesium alloys">magnesium alloys</a>, <a href="https://publications.waset.org/abstracts/search?q=fatigue%20crack%20propagation%20model" title=" fatigue crack propagation model"> fatigue crack propagation model</a>, <a href="https://publications.waset.org/abstracts/search?q=load%20ratio" title=" load ratio"> load ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=interpolation%20of%20random%20variable" title=" interpolation of random variable"> interpolation of random variable</a> </p> <a href="https://publications.waset.org/abstracts/5560/estimation-of-probabilistic-fatigue-crack-propagation-models-of-az31-magnesium-alloys-under-various-load-ratio-conditions-by-using-the-interpolation-of-a-random-variable" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5560.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">410</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">7060</span> Forecasting the Volatility of Geophysical Time Series with Stochastic Volatility Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maria%20C.%20Mariani">Maria C. Mariani</a>, <a href="https://publications.waset.org/abstracts/search?q=Md%20Al%20Masum%20Bhuiyan"> Md Al Masum Bhuiyan</a>, <a href="https://publications.waset.org/abstracts/search?q=Osei%20K.%20Tweneboah"> Osei K. Tweneboah</a>, <a href="https://publications.waset.org/abstracts/search?q=Hector%20G.%20Huizar"> Hector G. Huizar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work is devoted to the study of modeling geophysical time series. A stochastic technique with time-varying parameters is used to forecast the volatility of data arising in geophysics. In this study, the volatility is defined as a logarithmic first-order autoregressive process. We observe that the inclusion of log-volatility into the time-varying parameter estimation significantly improves forecasting which is facilitated via maximum likelihood estimation. This allows us to conclude that the estimation algorithm for the corresponding one-step-ahead suggested volatility (with ±2 standard prediction errors) is very feasible since it possesses good convergence properties. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Augmented%20Dickey%20Fuller%20Test" title="Augmented Dickey Fuller Test">Augmented Dickey Fuller Test</a>, <a href="https://publications.waset.org/abstracts/search?q=geophysical%20time%20series" title=" geophysical time series"> geophysical time series</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20volatility%20model" title=" stochastic volatility model"> stochastic volatility model</a> </p> <a href="https://publications.waset.org/abstracts/75110/forecasting-the-volatility-of-geophysical-time-series-with-stochastic-volatility-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75110.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">315</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">7059</span> Numerical Simulation of Wishart Diffusion Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Raphael%20Naryongo">Raphael Naryongo</a>, <a href="https://publications.waset.org/abstracts/search?q=Philip%20%20Ngare"> Philip Ngare</a>, <a href="https://publications.waset.org/abstracts/search?q=Anthony%20%20Waititu"> Anthony Waititu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with numerical simulation of Wishart processes for a single asset risky pricing model whose volatility is described by Wishart affine diffusion processes. The multi-factor specification of volatility will make the model more flexible enough to fit the stock market data for short or long maturities for better returns. The Wishart process is a stochastic process which is a positive semi-definite matrix-valued generalization of the square root process. The aim of the study is to model the log asset stock returns under the double Wishart stochastic volatility model. The solution of the log-asset return dynamics for Bi-Wishart processes will be obtained through Euler-Maruyama discretization schemes. The numerical results on the asset returns are compared to the existing models returns such as Heston stochastic volatility model and double Heston stochastic volatility model <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=euler%20schemes" title="euler schemes">euler schemes</a>, <a href="https://publications.waset.org/abstracts/search?q=log-asset%20return" title=" log-asset return"> log-asset return</a>, <a href="https://publications.waset.org/abstracts/search?q=infinitesimal%20generator" title=" infinitesimal generator"> infinitesimal generator</a>, <a href="https://publications.waset.org/abstracts/search?q=wishart%20diffusion%20affine%20processes" title=" wishart diffusion affine processes "> wishart diffusion affine processes </a> </p> <a href="https://publications.waset.org/abstracts/137631/numerical-simulation-of-wishart-diffusion-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137631.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">378</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">7058</span> Stochastic Energy and Reserve Scheduling with Wind Generation and Generic Energy Storage Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amirhossein%20Khazali">Amirhossein Khazali</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Kalantar"> Mohsen Kalantar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Energy storage units can play an important role to provide an economic and secure operation of future energy systems. In this paper, a stochastic energy and reserve market clearing scheme is presented considering storage energy units. The approach is proposed to deal with stochastic and non-dispatchable renewable sources with a high level of penetration in the energy system. A two stage stochastic programming scheme is formulated where in the first stage the energy market is cleared according to the forecasted amount of wind generation and demands and in the second stage the real time market is solved according to the assumed scenarios. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20and%20reserve%20market" title="energy and reserve market">energy and reserve market</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20storage%20device" title=" energy storage device"> energy storage device</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20programming" title=" stochastic programming"> stochastic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=wind%20generation" title=" wind generation"> wind generation</a> </p> <a href="https://publications.waset.org/abstracts/36215/stochastic-energy-and-reserve-scheduling-with-wind-generation-and-generic-energy-storage-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36215.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">574</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">7057</span> Sufficient Conditions for Exponential Stability of Stochastic Differential Equations with Non Trivial Solutions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fakhreddin%20Abedi">Fakhreddin Abedi</a>, <a href="https://publications.waset.org/abstracts/search?q=Wah%20June%20Leong"> Wah June Leong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Exponential stability of stochastic differential equations with non trivial solutions is provided in terms of Lyapunov functions. The main result of this paper establishes that, under certain hypotheses for the dynamics f(.) and g(.), practical exponential stability in probability at the small neighborhood of the origin is equivalent to the existence of an appropriate Lyapunov function. Indeed, we establish exponential stability of stochastic differential equation when almost all the state trajectories are bounded and approach a sufficiently small neighborhood of the origin. We derive sufficient conditions for exponential stability of stochastic differential equations. Finally, we give a numerical example illustrating our results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exponential%20stability%20in%20probability" title="exponential stability in probability">exponential stability in probability</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20differential%20equations" title=" stochastic differential equations"> stochastic differential equations</a>, <a href="https://publications.waset.org/abstracts/search?q=Lyapunov%20technique" title=" Lyapunov technique"> Lyapunov technique</a>, <a href="https://publications.waset.org/abstracts/search?q=Ito%27s%20formula" title=" Ito's formula"> Ito's formula</a> </p> <a href="https://publications.waset.org/abstracts/184321/sufficient-conditions-for-exponential-stability-of-stochastic-differential-equations-with-non-trivial-solutions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184321.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">52</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">7056</span> A Neural Network Approach to Understanding Turbulent Jet Formations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nurul%20Bin%20Ibrahim">Nurul Bin Ibrahim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Advancements in neural networks have offered valuable insights into Fluid Dynamics, notably in addressing turbulence-related challenges. In this research, we introduce multiple applications of models of neural networks, namely Feed-Forward and Recurrent Neural Networks, to explore the relationship between jet formations and stratified turbulence within stochastically excited Boussinesq systems. Using machine learning tools like TensorFlow and PyTorch, the study has created models that effectively mimic and show the underlying features of the complex patterns of jet formation and stratified turbulence. These models do more than just help us understand these patterns; they also offer a faster way to solve problems in stochastic systems, improving upon traditional numerical techniques to solve stochastic differential equations such as the Euler-Maruyama method. In addition, the research includes a thorough comparison with the Statistical State Dynamics (SSD) approach, which is a well-established method for studying chaotic systems. This comparison helps evaluate how well neural networks can help us understand the complex relationship between jet formations and stratified turbulence. The results of this study underscore the potential of neural networks in computational physics and fluid dynamics, opening up new possibilities for more efficient and accurate simulations in these fields. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title="neural networks">neural networks</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=computational%20fluid%20dynamics" title=" computational fluid dynamics"> computational fluid dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20systems" title=" stochastic systems"> stochastic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=stratified%20turbulence" title=" stratified turbulence"> stratified turbulence</a> </p> <a href="https://publications.waset.org/abstracts/171124/a-neural-network-approach-to-understanding-turbulent-jet-formations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171124.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">70</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">7055</span> The Martingale Options Price Valuation for European Puts Using Stochastic Differential Equation Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20C.%20Chinwenyi">H. C. Chinwenyi</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20D.%20Ibrahim"> H. D. Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20A.%20Ahmed"> F. A. Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In modern financial mathematics, valuing derivatives such as options is often a tedious task. This is simply because their fair and correct prices in the future are often probabilistic. This paper examines three different Stochastic Differential Equation (SDE) models in finance; the Constant Elasticity of Variance (CEV) model, the Balck-Karasinski model, and the Heston model. The various Martingales option price valuation formulas for these three models were obtained using the replicating portfolio method. Also, the numerical solution of the derived Martingales options price valuation equations for the SDEs models was carried out using the Monte Carlo method which was implemented using MATLAB. Furthermore, results from the numerical examples using published data from the Nigeria Stock Exchange (NSE), all share index data show the effect of increase in the underlying asset value (stock price) on the value of the European Put Option for these models. From the results obtained, we see that an increase in the stock price yields a decrease in the value of the European put option price. Hence, this guides the option holder in making a quality decision by not exercising his right on the option. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=equivalent%20martingale%20measure" title="equivalent martingale measure">equivalent martingale measure</a>, <a href="https://publications.waset.org/abstracts/search?q=European%20put%20option" title=" European put option"> European put option</a>, <a href="https://publications.waset.org/abstracts/search?q=girsanov%20theorem" title=" girsanov theorem"> girsanov theorem</a>, <a href="https://publications.waset.org/abstracts/search?q=martingales" title=" martingales"> martingales</a>, <a href="https://publications.waset.org/abstracts/search?q=monte%20carlo%20method" title=" monte carlo method"> monte carlo method</a>, <a href="https://publications.waset.org/abstracts/search?q=option%20price%20valuation%20formula" title=" option price valuation formula"> option price valuation formula</a> </p> <a href="https://publications.waset.org/abstracts/111011/the-martingale-options-price-valuation-for-european-puts-using-stochastic-differential-equation-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/111011.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">133</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">7054</span> Prediction of Gully Erosion with Stochastic Modeling by using Geographic Information System and Remote Sensing Data in North of Iran</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Reza%20Zakerinejad">Reza Zakerinejad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gully erosion is a serious problem that threading the sustainability of agricultural area and rangeland and water in a large part of Iran. This type of water erosion is the main source of sedimentation in many catchment areas in the north of Iran. Since in many national assessment approaches just qualitative models were applied the aim of this study is to predict the spatial distribution of gully erosion processes by means of detail terrain analysis and GIS -based logistic regression in the loess deposition in a case study in the Golestan Province. This study the DEM with 25 meter result ion from ASTER data has been used. The Landsat ETM data have been used to mapping of land use. The TreeNet model as a stochastic modeling was applied to prediction the susceptible area for gully erosion. In this model ROC we have set 20 % of data as learning and 20 % as learning data. Therefore, applying the GIS and satellite image analysis techniques has been used to derive the input information for these stochastic models. The result of this study showed a high accurate map of potential for gully erosion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=TreeNet%20model" title="TreeNet model">TreeNet model</a>, <a href="https://publications.waset.org/abstracts/search?q=terrain%20analysis" title=" terrain analysis"> terrain analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Golestan%20Province" title=" Golestan Province"> Golestan Province</a>, <a href="https://publications.waset.org/abstracts/search?q=Iran" title=" Iran"> Iran</a> </p> <a href="https://publications.waset.org/abstracts/27028/prediction-of-gully-erosion-with-stochastic-modeling-by-using-geographic-information-system-and-remote-sensing-data-in-north-of-iran" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27028.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">7053</span> Finding DEA Targets Using Multi-Objective Programming</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farzad%20Sharifi">Farzad Sharifi</a>, <a href="https://publications.waset.org/abstracts/search?q=Raziyeh%20Shamsi"> Raziyeh Shamsi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we obtain the projection of inefficient units in data envelopment analysis (DEA) in the case of stochastic inputs and outputs using the multi-objective programming (MOP) structure. In some problems, the inputs might be stochastic while the outputs are deterministic, and vice versa. In such cases, we propose molti-objective DEA-R model, because in some cases (e.g., when unnecessary and irrational weights by the BCC model reduces the efficiency score), an efficient DMU is introduced as inefficient by the BCC model, whereas the DMU is considered efficient by the DEA-R model. In some other case, only the ratio of stochastic data may be available (e.g; the ratio of stochastic inputs to stochastic outputs). Thus, we provide multi objective DEA model without explicit outputs and prove that in-put oriented MOP DEA-R model in the invariable return to scale case can be replacing by MOP- DEA model without explicit outputs in the variable return to scale and vice versa. Using the interactive methods for solving the proposed model, yields a projection corresponding to the viewpoint of the DM and the analyst, which is nearer to reality and more practical. Finally, an application is provided. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DEA" title="DEA">DEA</a>, <a href="https://publications.waset.org/abstracts/search?q=MOLP" title=" MOLP"> MOLP</a>, <a href="https://publications.waset.org/abstracts/search?q=STOCHASTIC" title=" STOCHASTIC"> STOCHASTIC</a>, <a href="https://publications.waset.org/abstracts/search?q=DEA-R" title=" DEA-R"> DEA-R</a> </p> <a href="https://publications.waset.org/abstracts/59723/finding-dea-targets-using-multi-objective-programming" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59723.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">398</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">7052</span> Finding Data Envelopment Analysis Targets Using Multi-Objective Programming in DEA-R with Stochastic Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Shamsi">R. Shamsi</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Sharifi"> F. Sharifi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we obtain the projection of inefficient units in data envelopment analysis (DEA) in the case of stochastic inputs and outputs using the multi-objective programming (MOP) structure. In some problems, the inputs might be stochastic while the outputs are deterministic, and vice versa. In such cases, we propose a multi-objective DEA-R model because in some cases (e.g., when unnecessary and irrational weights by the BCC model reduce the efficiency score), an efficient decision-making unit (DMU) is introduced as inefficient by the BCC model, whereas the DMU is considered efficient by the DEA-R model. In some other cases, only the ratio of stochastic data may be available (e.g., the ratio of stochastic inputs to stochastic outputs). Thus, we provide a multi-objective DEA model without explicit outputs and prove that the input-oriented MOP DEA-R model in the invariable return to scale case can be replaced by the MOP-DEA model without explicit outputs in the variable return to scale and vice versa. Using the interactive methods for solving the proposed model yields a projection corresponding to the viewpoint of the DM and the analyst, which is nearer to reality and more practical. Finally, an application is provided. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DEA-R" title="DEA-R">DEA-R</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20programming" title=" multi-objective programming"> multi-objective programming</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20data" title=" stochastic data"> stochastic data</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20envelopment%20analysis" title=" data envelopment analysis"> data envelopment analysis</a> </p> <a href="https://publications.waset.org/abstracts/154613/finding-data-envelopment-analysis-targets-using-multi-objective-programming-in-dea-r-with-stochastic-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154613.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">105</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">7051</span> Testing and Validation Stochastic Models in Epidemiology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Snigdha%20Sahai">Snigdha Sahai</a>, <a href="https://publications.waset.org/abstracts/search?q=Devaki%20Chikkavenkatappa%20Yellappa"> Devaki Chikkavenkatappa Yellappa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study outlines approaches for testing and validating stochastic models used in epidemiology, focusing on the integration and functional testing of simulation code. It details methods for combining simple functions into comprehensive simulations, distinguishing between deterministic and stochastic components, and applying tests to ensure robustness. Techniques include isolating stochastic elements, utilizing large sample sizes for validation, and handling special cases. Practical examples are provided using R code to demonstrate integration testing, handling of incorrect inputs, and special cases. The study emphasizes the importance of both functional and defensive programming to enhance code reliability and user-friendliness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computational%20epidemiology" title="computational epidemiology">computational epidemiology</a>, <a href="https://publications.waset.org/abstracts/search?q=epidemiology" title=" epidemiology"> epidemiology</a>, <a href="https://publications.waset.org/abstracts/search?q=public%20health" title=" public health"> public health</a>, <a href="https://publications.waset.org/abstracts/search?q=infectious%20disease%20modeling" title=" infectious disease modeling"> infectious disease modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20analysis" title=" statistical analysis"> statistical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20data%20analysis" title=" health data analysis"> health data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=disease%20transmission%20dynamics" title=" disease transmission dynamics"> disease transmission dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20modeling%20in%20health" title=" predictive modeling in health"> predictive modeling in health</a>, <a href="https://publications.waset.org/abstracts/search?q=population%20health%20modeling" title=" population health modeling"> population health modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=quantitative%20public%20health" title=" quantitative public health"> quantitative public health</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20sampling%20simulations" title=" random sampling simulations"> random sampling simulations</a>, <a href="https://publications.waset.org/abstracts/search?q=randomized%20numerical%20analysis" title=" randomized numerical analysis"> randomized numerical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation-based%20analysis" title=" simulation-based analysis"> simulation-based analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=variance-based%20simulations" title=" variance-based simulations"> variance-based simulations</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithmic%20disease%20simulation" title=" algorithmic disease simulation"> algorithmic disease simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20public%20health%20strategies" title=" computational public health strategies"> computational public health strategies</a>, <a href="https://publications.waset.org/abstracts/search?q=epidemiological%20surveillance" title=" epidemiological surveillance"> epidemiological surveillance</a>, <a href="https://publications.waset.org/abstracts/search?q=disease%20pattern%20analysis" title=" disease pattern analysis"> disease pattern analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=epidemic%20risk%20assessment" title=" epidemic risk assessment"> epidemic risk assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=population-based%20health%20strategies" title=" population-based health strategies"> population-based health strategies</a>, <a href="https://publications.waset.org/abstracts/search?q=preventive%20healthcare%20models" title=" preventive healthcare models"> preventive healthcare models</a>, <a href="https://publications.waset.org/abstracts/search?q=infection%20dynamics%20in%20populations" title=" infection dynamics in populations"> infection dynamics in populations</a>, <a href="https://publications.waset.org/abstracts/search?q=contagion%20spread%20prediction%20models" title=" contagion spread prediction models"> contagion spread prediction models</a>, <a href="https://publications.waset.org/abstracts/search?q=survival%20analysis%20techniques" title=" survival analysis techniques"> survival analysis techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=epidemiological%20data%20mining" title=" epidemiological data mining"> epidemiological data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=host-pathogen%20interaction%20models" title=" host-pathogen interaction models"> host-pathogen interaction models</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20assessment%20algorithms%20for%20disease%20spread" title=" risk assessment algorithms for disease spread"> risk assessment algorithms for disease spread</a>, <a href="https://publications.waset.org/abstracts/search?q=decision-support%20systems%20in%20epidemiology" title=" decision-support systems in epidemiology"> decision-support systems in epidemiology</a>, <a href="https://publications.waset.org/abstracts/search?q=macro-level%20health%20impact%20simulations" title=" macro-level health impact simulations"> macro-level health impact simulations</a>, <a href="https://publications.waset.org/abstracts/search?q=socioeconomic%20determinants%20in%20disease%20spread" title=" socioeconomic determinants in disease spread"> socioeconomic determinants in disease spread</a>, <a href="https://publications.waset.org/abstracts/search?q=data-driven%20decision%20making%20in%20public%20health" title=" data-driven decision making in public health"> data-driven decision making in public health</a>, <a href="https://publications.waset.org/abstracts/search?q=quantitative%20impact%20assessment%20of%20health%20policies" title=" quantitative impact assessment of health policies"> quantitative impact assessment of health policies</a>, <a href="https://publications.waset.org/abstracts/search?q=biostatistical%20methods%20in%20population%20health" title=" biostatistical methods in population health"> biostatistical methods in population health</a>, <a href="https://publications.waset.org/abstracts/search?q=probability-driven%20health%20outcome%20predictions" title=" probability-driven health outcome predictions"> probability-driven health outcome predictions</a> </p> <a href="https://publications.waset.org/abstracts/194780/testing-and-validation-stochastic-models-in-epidemiology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194780.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">6</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=stochastic%20models&page=2">2</a></li> <li class="page-item"><a class="page-link" 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