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Journal articles on the topic 'Bayesian Modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 28 September 2024

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1

Qiao, Xin, and Hong Jiao. "Bayesian Psychometric Modeling." Measurement: Interdisciplinary Research and Perspectives 16, no. 2 (March 30, 2018): 135–37. http://dx.doi.org/10.1080/15366367.2018.1437307.

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2

Hicks, Tyler, Liliana Rodríguez-Campos, and Jeong Hoon Choi. "Bayesian Posterior Odds Ratios." American Journal of Evaluation 39, no. 2 (May 23, 2017): 278–89. http://dx.doi.org/10.1177/1098214017704302.

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To begin statistical analysis, Bayesians quantify their confidence in modeling hypotheses with priors. A prior describes the probability of a certain modeling hypothesis apart from the data. Bayesians should be able to defend their choice of prior to a skeptical audience. Collaboration between evaluators and stakeholders could make their choices more defensible. This article describes how evaluators and stakeholders could combine their expertise to select rigorous priors for analysis. The article first introduces Bayesian testing, then situates it within a collaborative framework, and finally illustrates the method with a real example.

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3

Gelman, Andrew. "Parameterization and Bayesian Modeling." Journal of the American Statistical Association 99, no. 466 (June 2004): 537–45. http://dx.doi.org/10.1198/016214504000000458.

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4

Ghosh, Subir. "Probability and Bayesian Modeling." Technometrics 62, no. 3 (July 2, 2020): 415–16. http://dx.doi.org/10.1080/00401706.2020.1783947.

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5

Robert, Christian, and Ioannis Ntzoufras. "Bayesian Modeling Using WinBUGS." CHANCE 25, no. 2 (April 16, 2012): 60–61. http://dx.doi.org/10.1080/09332480.2012.685377.

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6

Dunson, David B. "Bayesian nonparametric hierarchical modeling." Biometrical Journal 51, no. 2 (April 2009): 273–84. http://dx.doi.org/10.1002/bimj.200800183.

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7

Montes-Rojas, Gabriel, and Antonio F. Galvao. "Bayesian endogeneity bias modeling." Economics Letters 122, no. 1 (January 2014): 36–39. http://dx.doi.org/10.1016/j.econlet.2013.10.034.

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8

Ziegel, Eric. "Bayesian Thinking: Modeling and Computation." Technometrics 48, no. 4 (November 2006): 576–77. http://dx.doi.org/10.1198/tech.2006.s445.

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9

Kottas, Athanasios, and Alan E. Gelfand. "Bayesian Semiparametric Median Regression Modeling." Journal of the American Statistical Association 96, no. 456 (December 2001): 1458–68. http://dx.doi.org/10.1198/016214501753382363.

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10

Palacios, M. Blanca, and Mark F. J. Steel. "Non-Gaussian Bayesian Geostatistical Modeling." Journal of the American Statistical Association 101, no. 474 (June 1, 2006): 604–18. http://dx.doi.org/10.1198/016214505000001195.

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11

Wang, Min, and Ji-Chun Liu. "Bayesian Semiparametric Double Autoregressive Modeling." Mathematical Problems in Engineering 2019 (July 15, 2019): 1–9. http://dx.doi.org/10.1155/2019/4267532.

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This paper proposes a Bayesian semiparametric modeling approach for the return distribution in double autoregressive models. Monte Carlo investigation of finite sample properties and an empirical application are presented. The results indicate that the semiparametric model developed in this paper is valuable and competitive.

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12

Casanova, María P., Pilar Iglesias, Heleno Bolfarine, Victor H. Salinas, and Alexis Peña. "Semiparametric Bayesian measurement error modeling." Journal of Multivariate Analysis 101, no. 3 (March 2010): 512–24. http://dx.doi.org/10.1016/j.jmva.2009.11.004.

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13

Al-Shawaf, Laith, and David Buss. "Evolutionary psychology and Bayesian modeling." Behavioral and Brain Sciences 34, no. 4 (August 2011): 188–89. http://dx.doi.org/10.1017/s0140525x11000173.

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AbstractThe target article provides important theoretical contributions to psychology and Bayesian modeling. Despite the article's excellent points, we suggest that it succumbs to a few misconceptions about evolutionary psychology (EP). These include a mischaracterization of evolutionary psychology's approach to optimality; failure to appreciate the centrality of mechanism in EP; and an incorrect depiction of hypothesis testing. An accurate characterization of EP offers more promise for successful integration with Bayesian modeling.

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14

Fenn, Timothy, Michael Schnieders, and Vijay Pande. "Bayesian Modeling of Crystallographic Disorder." Biophysical Journal 102, no. 3 (January 2012): 225a. http://dx.doi.org/10.1016/j.bpj.2011.11.1235.

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15

Inácio de Carvalho, Vanda, Alejandro Jara, Timothy E. Hanson, and Miguel de Carvalho. "Bayesian Nonparametric ROC Regression Modeling." Bayesian Analysis 8, no. 3 (September 2013): 623–46. http://dx.doi.org/10.1214/13-ba825.

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16

Guthrie, William F. "Bayesian Statistical Modeling, 2nd edition." Journal of Quality Technology 41, no. 3 (July 2009): 317–18. http://dx.doi.org/10.1080/00224065.2009.11917785.

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17

Jensen, Mark J., and John M. Maheu. "Bayesian semiparametric stochastic volatility modeling." Journal of Econometrics 157, no. 2 (August 2010): 306–16. http://dx.doi.org/10.1016/j.jeconom.2010.01.014.

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18

Jensen, Mark J., and John M. Maheu. "Bayesian semiparametric multivariate GARCH modeling." Journal of Econometrics 176, no. 1 (September 2013): 3–17. http://dx.doi.org/10.1016/j.jeconom.2013.03.009.

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19

Park, Eun Sug, and Man-Suk Oh. "Robust Bayesian multivariate receptor modeling." Chemometrics and Intelligent Laboratory Systems 149 (December 2015): 215–26. http://dx.doi.org/10.1016/j.chemolab.2015.08.021.

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20

Park, Eun Sug, and Man-Suk Oh. "Bayesian quantile multivariate receptor modeling." Chemometrics and Intelligent Laboratory Systems 159 (December 2016): 174–80. http://dx.doi.org/10.1016/j.chemolab.2016.10.008.

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21

Zaidi, Abdelaziz, Belkacem Ould Bouamama, and Moncef Tagina. "Bayesian reliability models of Weibull systems: State of the art." International Journal of Applied Mathematics and Computer Science 22, no. 3 (September 1, 2012): 585–600. http://dx.doi.org/10.2478/v10006-012-0045-2.

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Abstract In the reliability modeling field, we sometimes encounter systems with uncertain structures, and the use of fault trees and reliability diagrams is not possible. To overcome this problem, Bayesian approaches offer a considerable efficiency in this context. This paper introduces recent contributions in the field of reliability modeling with the Bayesian network approach. Bayesian reliability models are applied to systems with Weibull distribution of failure. To achieve the formulation of the reliability model, Bayesian estimation of Weibull parameters and the model’s goodness-of-fit are evoked. The advantages of this modelling approach are presented in the case of systems with an unknown reliability structure, those with a common cause of failures and redundant ones. Finally, we raise the issue of the use of BNs in the fault diagnosis area.

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22

Akimov, Valery. "Natural emergency modeling and forecasting." XXI century. Technosphere Safety 9, no. 1 (January 29, 2024): 100–108. http://dx.doi.org/10.21285/2500-1582-2024-9-1-100-108.

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The purpose of this study is to develop forecast and analytical models (FAM) for predicting the most catastrophic natural emergencies caused by floods, earthquakes and forest fires. The article discusses predictive and analytical solutions for natural hazards for urbanized areas based on the Bayesian classifiers. The result is a formalized description of models for predicting forest fires, consequences of earthquakes and floods. The novelty of the models is due to the application of a unified scientific approach - the statistical processing method based on Bayes’ theorem. In contrast to the frequency probability determined by the relative frequency of occurrence of random events over sufficiently long observations, the Bayesian probability is the main forecasting method for constructing and training neural networks. Scientific forecasting of crises and incidents based on the Bayesian method and Bayesian networks requires a large amount of up-to-date data to model natural disasters, which is typical for frequently recurring negative events. Due to the lack of statistical data, the Bayesian method is not applicable to predicting catastrophic natural disasters that occur rarely but cause significant damage.

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23

Mimouni, Faycal, and Abdellah Abouabdellah. "Proposition of a modeling and an analysis methodology of integrated reverse logistics chain in the direct chain." Journal of Industrial Engineering and Management 9, no. 2 (April 26, 2016): 359. http://dx.doi.org/10.3926/jiem.1720.

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Purpose: Propose a modeling and analysis methodology based on the combination of Bayesian networks and Petri networks of the reverse logistics integrated the direct supply chain.Design/methodology/approach: Network modeling by combining Petri and Bayesian network.Findings: Modeling with Bayesian network complimented with Petri network to break the cycle problem in the Bayesian network.Research limitations/implications: Demands are independent from returns.Practical implications: Model can only be used on nonperishable products.Social implications: Legislation aspects: Recycling laws; Protection of environment; Client satisfaction via after sale service.Originality/value: Bayesian network with a cycle combined with the Petri Network.

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24

Hamilton, W. Derek, and Anthony M. Krus. "THE MYTHS AND REALITIES OF BAYESIAN CHRONOLOGICAL MODELING REVEALED." American Antiquity 83, no. 2 (November 6, 2017): 187–203. http://dx.doi.org/10.1017/aaq.2017.57.

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We review the history of Bayesian chronological modeling in archaeology and demonstrate that there has been a surge over the past several years in American archaeological applications. Most of these applications have been performed by archaeologists who are self-taught in this method because formal training opportunities in Bayesian chronological modeling are infrequently provided. We define and address misconceptions about Bayesian chronological modeling that we have encountered in conversations with colleagues and in anonymous reviews, some of which have been expressed in the published literature. Objectivity and scientific rigor is inherent in the Bayesian chronological modeling process. Each stage of this process is described in detail, and we present examples of this process in practice. Our concluding discussion focuses on the potential that Bayesian chronological modeling has for enhancing understandings of important topics.

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25

Berg, Arthur. "Bayesian Modeling Competitions for the Classroom." Revista Colombiana de Estadística 44, no. 2 (July 12, 2021): 243–52. http://dx.doi.org/10.15446/rce.v44n2.89102.

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Three educational and engaging competitions are described for students studying Bayesian statistics. These competitions are designed to help students explore the topics of James-Stein estimation, the German tank problem, and resampling inference. These competitions will inspire students to think creatively, challenge students to develop effective Bayesian models, and motivate students to pursue excellence in competition with their peers. The competition structures can be easily adapted for use in introductory or advanced Bayesian statistics courses.

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26

Salokekkilä, Liisa, and Saara Ryynänen. "INSIGHTS INTO STUDENT PERSPECTIVES: BAYESIAN MODELING OF LEARNING ENVIRONMENTS AND STUDY APPROACHES." International Journal of Prevention Practice and Research 04, no. 02 (February 1, 2024): 01–07. http://dx.doi.org/10.55640/medscience-abcd632.

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Understanding student perspectives on their learning environments and study approaches is crucial for designing effective educational interventions and fostering academic success. This study employs Bayesian modeling techniques to analyze data gathered from student surveys, exploring the complex relationships between various factors influencing learning experiences and study habits. By leveraging Bayesian inference, the study offers insights into the nuanced interactions between student characteristics, learning environments, and academic outcomes. The findings provide valuable guidance for educators and policymakers seeking to optimize learning environments and support students in achieving their educational goals.

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27

Liu, Yi Jian, Li Ming Di, and Yan Jun Fang. "On-Line Modeling of Dynamic Nonlinear System Based on Bayesian Inferring Method Combined with Evolutionary Algorithms." Advanced Materials Research 532-533 (June 2012): 1640–44. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1640.

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A novel modeling method based on Bayesian inferring for dynamic nonlinear system is proposed in this article. The Bayesian inferring model structure and its training algorithm combined with evolutionary algorithms are first described in which the matrix threshold D parameters are optimized by evolutionary algorithms and the structure of the Bayesian inferring model is updated by the system running data. Then some typical dynamic systems are used for validating the modeling effectiveness of the Bayesian inferring method. And the simulation results are presented and some conclusion on the Bayesian inferring modeling method is described in details at last.

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28

Zhang, Yanwei. "BAYESIAN ANALYSIS OF BIG DATA IN INSURANCE PREDICTIVE MODELING USING DISTRIBUTED COMPUTING." ASTIN Bulletin 47, no. 3 (July 6, 2017): 943–61. http://dx.doi.org/10.1017/asb.2017.15.

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AbstractWhile Bayesian methods have attracted considerable interest in actuarial science, they are yet to be embraced in large-scaled insurance predictive modeling applications, due to inefficiencies of Bayesian estimation procedures. The paper presents an efficient method that parallelizes Bayesian computation using distributed computing on Apache Spark across a cluster of computers. The distributed algorithm dramatically boosts the speed of Bayesian computation and expands the scope of applicability of Bayesian methods in insurance modeling. The empirical analysis applies a Bayesian hierarchical Tweedie model to a big data of 13 million insurance claim records. The distributed algorithm achieves as much as 65 times performance gain over the non-parallel method in this application. The analysis demonstrates that Bayesian methods can be of great value to large-scaled insurance predictive modeling.

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29

Shimamura, Kaito, Shuichi Kawano, and Sadanori Konishi. "Bayesian Lasso Regression Modeling via Model Averaging." Japanese Journal of Applied Statistics 44, no. 3 (2015): 101–17. http://dx.doi.org/10.5023/jappstat.44.101.

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30

Stenling, Andreas, Andreas Ivarsson, Urban Johnson, and Magnus Lindwall. "Bayesian Structural Equation Modeling in Sport and Exercise Psychology." Journal of Sport and Exercise Psychology 37, no. 4 (August 2015): 410–20. http://dx.doi.org/10.1123/jsep.2014-0330.

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Bayesian statistics is on the rise in mainstream psychology, but applications in sport and exercise psychology research are scarce. In this article, the foundations of Bayesian analysis are introduced, and we will illustrate how to apply Bayesian structural equation modeling in a sport and exercise psychology setting. More specifically, we contrasted a confirmatory factor analysis on the Sport Motivation Scale II estimated with the most commonly used estimator, maximum likelihood, and a Bayesian approach with weakly informative priors for cross-loadings and correlated residuals. The results indicated that the model with Bayesian estimation and weakly informative priors provided a good fit to the data, whereas the model estimated with a maximum likelihood estimator did not produce a well-fitting model. The reasons for this discrepancy between maximum likelihood and Bayesian estimation are discussed as well as potential advantages and caveats with the Bayesian approach.

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31

Haverkorn, Marijke, François Boulanger, Torsten Enßlin, Jörg Hörandel, Tess Jaffe, Jens Jasche, Jörg Rachen, and Anvar Shukurov. "IMAGINE: Modeling the Galactic Magnetic Field." Galaxies 7, no. 1 (January 14, 2019): 17. http://dx.doi.org/10.3390/galaxies7010017.

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The IMAGINE Consortium aims to bring modeling of the magnetic field of the Milky Way to the next level by using Bayesian inference. IMAGINE includes an open-source modular software pipeline that optimizes parameters in a user-defined galactic magnetic field model against various selected observational datasets. Bayesian priors can be added as external probabilistic constraints of the model parameters. These conference proceedings describe the science goals of the IMAGINE consortium, the software pipeline and its inputs, namely observational data sets, galactic magnetic field models, and Bayesian priors.

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32

Zvonok, A. A. "Bayesian modeling of binomial experiments in sociology: problem analysis." Digital Sociology 7, no. 1 (April 23, 2024): 14–25. http://dx.doi.org/10.26425/2658-347x-2024-7-1-14-25.

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The article is devoted to Bayesian modeling of simple comparative binomial experiments with binary data sets (of “hit” and “miss” format) in sociology and other social sciences. The main methodological foundations of application of Bayesian approach in statistics are briefly reviewed: the use of priors in analysis, features of Bayesian statistical inference, differences in frequency and Bayesian confidence intervals, features of hypothesis testing in Bayesian statistics. A Bayesian model of a comparative binomial experiment has been constructed. It supports comparison of independent and dependent samples of binomial variables, and also allows for differences in sizes of the compared samples. The capabilities of the model, as well as the principles of the Bayesian hypothesis testing, were demonstrated on test data using PyMC and ArviZ, contemporary free packages of the Bayesian modeling and analysis. The use of these tools allows implementing direct tensor operations with the obtained posterior distributions and provides the researcher with an effective way to calculate the effect size when comparing two binomial samples without having to resort to complicated forms of calculating this parameter. The possibilities and limitations of the Bayesian approach are shown in the context of comparative analysis of the results of binomial experiments in social sciences by estimating the probability of hypotheses via finding and comparing the area of intervals of posterior distributions

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33

TERZIYAN, VAGAN. "A BAYESIAN METANETWORK." International Journal on Artificial Intelligence Tools 14, no. 03 (June 2005): 371–84. http://dx.doi.org/10.1142/s0218213005002156.

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Bayesian network (BN) is known to be one of the most solid probabilistic modeling tools. The theory of BN provides already several useful modifications of a classical network. Among those there are context-enabled networks such as multilevel networks or recursive multinets, which can provide separate BN modelling for different combinations of contextual features' values. The main challenge of this paper is the multilevel probabilistic meta-model (Bayesian Metanetwork), which is an extension of traditional BN and modification of recursive multinets. It assumes that interoperability between component networks can be modeled by another BN. Bayesian Metanetwork is a set of BN, which are put on each other in such a way that conditional or unconditional probability distributions associated with nodes of every previous probabilistic network depend on probability distributions associated with nodes of the next network. We assume parameters (probability distributions) of a BN as random variables and allow conditional dependencies between these probabilities. Several cases of two-level Bayesian Metanetworks were presented, which consist on interrelated predictive and contextual BN models.

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34

Fisher, Georgiana, and Andrew B. Lawson. "Bayesian modeling of georeferenced cancer survival." Annals of Cancer Epidemiology 4 (June 2020): 6. http://dx.doi.org/10.21037/ace-19-32.

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35

WANG, Meng-Cheng, Qiaowen DENG, and Xiangyang BI. "Latent variable modeling using Bayesian methods." Advances in Psychological Science 25, no. 10 (2017): 1682. http://dx.doi.org/10.3724/sp.j.1042.2017.01682.

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36

Kim, Yongku. "Hierarchical Bayesian modeling for soil moisture." Journal of the Korean Data And Information Science Society 30, no. 4 (July 31, 2019): 713–21. http://dx.doi.org/10.7465/jkdi.2019.30.4.713.

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37

Lewis, R. J. "Bayesian Modeling and Real-world Problems." Academic Emergency Medicine 10, no. 7 (July 1, 2003): 780–82. http://dx.doi.org/10.1197/aemj.10.7.780.

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38

Hill, Jennifer L. "Bayesian Nonparametric Modeling for Causal Inference." Journal of Computational and Graphical Statistics 20, no. 1 (January 2011): 217–40. http://dx.doi.org/10.1198/jcgs.2010.08162.

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39

Chatterjee, Snigdhansu. "Structural Equation Modeling, A Bayesian Approach." Technometrics 50, no. 3 (August 2008): 411–12. http://dx.doi.org/10.1198/tech.2008.s907.

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40

Driver, Charles C., and Manuel C. Voelkle. "Hierarchical Bayesian continuous time dynamic modeling." Psychological Methods 23, no. 4 (December 2018): 774–99. http://dx.doi.org/10.1037/met0000168.

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41

De Iorio, Maria, Wesley O. Johnson, Peter Müller, and Gary L. Rosner. "Bayesian Nonparametric Nonproportional Hazards Survival Modeling." Biometrics 65, no. 3 (February 5, 2009): 762–71. http://dx.doi.org/10.1111/j.1541-0420.2008.01166.x.

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42

Roberts, S. J., D. Husmeier, I. Rezek, and W. Penny. "Bayesian approaches to Gaussian mixture modeling." IEEE Transactions on Pattern Analysis and Machine Intelligence 20, no. 11 (1998): 1133–42. http://dx.doi.org/10.1109/34.730550.

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43

van den Haak, W. P., L. J. M. Rothkrantz, P. Wiggers, B. M. R. Heijligers, T. Bakri, and D. Vukovic. "Modeling Traffic Information using Bayesian Networks." Transactions on Transport Sciences 3, no. 3 (September 1, 2010): 129–36. http://dx.doi.org/10.2478/v10158-010-0018-09.

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44

Lockamy, Archie, and Kevin McCormack. "Modeling supplier risks using Bayesian networks." Industrial Management & Data Systems 112, no. 2 (March 9, 2012): 313–33. http://dx.doi.org/10.1108/02635571211204317.

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45

Kaser, Tanja, Severin Klingler, Alexander G. Schwing, and Markus Gross. "Dynamic Bayesian Networks for Student Modeling." IEEE Transactions on Learning Technologies 10, no. 4 (October 1, 2017): 450–62. http://dx.doi.org/10.1109/tlt.2017.2689017.

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46

Thomas, Duncan C., Daniel O. Stram, David Conti, John Molitor, and Paul Marjoram. "Bayesian Spatial Modeling of Haplotype Associations." Human Heredity 56, no. 1-3 (2003): 32–40. http://dx.doi.org/10.1159/000073730.

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47

Conti, David V., Victoria Cortessis, John Molitor, and Duncan C. Thomas. "Bayesian Modeling of Complex Metabolic Pathways." Human Heredity 56, no. 1-3 (2003): 83–93. http://dx.doi.org/10.1159/000073736.

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48

Jelliffe, Roger, Michael Neely, Alan Schumitzky, David Bayard, Michael Van Guilder, Andreas Botnen, Aida Bustad, et al. "Nonparametric population modeling and Bayesian analysis." Pharmacological Research 64, no. 4 (October 2011): 426. http://dx.doi.org/10.1016/j.phrs.2011.04.008.

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49

Labatut, Vincent, Josette Pastor, Serge Ruff, Jean-François Démonet, and Pierre Celsis. "Cerebral modeling and dynamic Bayesian networks." Artificial Intelligence in Medicine 30, no. 2 (February 2004): 119–39. http://dx.doi.org/10.1016/s0933-3657(03)00042-3.

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50

Montagnini, Anna, Pascal Mamassian, Laurent Perrinet, Eric Castet, and Guillaume S. Masson. "Bayesian modeling of dynamic motion integration." Journal of Physiology-Paris 101, no. 1-3 (January 2007): 64–77. http://dx.doi.org/10.1016/j.jphysparis.2007.10.013.

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