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Books on the topic 'Bayesian models of generalization'

Author: Grafiati

Published: 4 June 2025

Last updated: 23 June 2025

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1

Kempthorne, Peter J. Bayesian parametric models. Alfred P. Sloan School of Management, Massachusetts Institute of Technology, 1989.

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2

Quintana, Jose Mario. Multivariate Bayesian forecasting models. typescript, 1987.

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3

Barber, David, A. Taylan Cemgil, and Silvia Chiappa, eds. Bayesian Time Series Models. Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511984679.

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4

Barber, David. Bayesian time series models. Cambridge University Press, 2011.

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5

Jansen, Paulus Gerardus Wilhelmus, 1954-, ed. Validity generalization revisited. Delft University Press, 1986.

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6

Hooten, Mevin B., and Trevor J. Hefley. Bringing Bayesian Models to Life. CRC Press, 2019. http://dx.doi.org/10.1201/9780429243653.

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7

Young, Simon Christopher. Bayesian models and repeated games. typescript, 1989.

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8

West, Mike, and Jeff Harrison. Bayesian Forecasting and Dynamic Models. Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4757-9365-9.

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9

Congdon, Peter. Bayesian Models for Categorical Data. John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470092394.

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10

Weber, Philippe, and Christophe Simon. Benefits of Bayesian Network Models. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119347316.

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11

Routis, J. Bayesian analysis of ARMA models. UMIST, 1997.

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12

West, Mike. Bayesian forecasting and dynamic models. Springer, 1989.

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13

Escobar, Michael D. Computing Bayesian nonparametic hierarchiacal models. University of Toronto, Dept. of Statistics, 1998.

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14

Jeff, Harrison, ed. Bayesian forecasting and dynamic models. 2nd ed. Springer, 1997.

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15

R, Lehmann Donald, ed. Meta-analysis in marketing: Generalization of response models. Lexington Books, 1986.

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16

Triantafyllopoulos, Kostas. Bayesian Inference of State Space Models. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76124-0.

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17

Müller, Peter, and Brani Vidakovic, eds. Bayesian Inference in Wavelet-Based Models. Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-0567-8.

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18

M, Wiper Mike, and Ríos Insua David 1964-, eds. Bayesian analysis of stochastic process models. Wiley, 2012.

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19

Dipak, Dey, Ghosh Sujit K. 1970-, and Mallick Bani K. 1965-, eds. Generalized linear models: A Bayesian perspective. Marcel Dekker, 2000.

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20

Bauwens, Luc. Bayesian inference in dynamic econometric models. Oxford University Press, 1999.

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21

Campolieti, Michele. Bayesian estimation of discrete duration models. National Library of Canada = Bibliothèque nationale du Canada, 1997.

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22

Press, S. James. Bayesian statistics: Principles, models, and applications. Wiley, 1989.

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23

Rossi, Peter E. Bayesian statistics and marketing. Wiley, 2005.

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24

T, Rachev S., ed. Bayesian methods in finance. Wiley, 2008.

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25

Gregoriou, Greg N., and Razvan Pascalau, eds. Nonlinear Financial Econometrics: Forecasting Models, Computational and Bayesian Models. Palgrave Macmillan UK, 2011. http://dx.doi.org/10.1057/9780230295223.

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26

Congdon, P. Applied Bayesian hierarchical methods. Chapman & Hall/CRC, 2010.

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27

Fernández-Villaverde, Jesús, Pablo Guerrón-Quintana, and Juan Rubio-Ramírez. Futures markets, Bayesian forecasting and risk modelling. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.14.

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This article demonstrates the utility of the Bayesian approach in forecasting and risk modelling regarding speculative trading strategies in financial futures markets. It first provides an overview of subjective expectations that are motivated as fair prices of futures contracts before discussing the futures markets and a portfolio mean-variance efficiency generalization. In particular, it considers the critical role of hedging to ensue attractive risk-adjusted performance. It also describes general Bayesian dynamic models and specific Bayesian dynamic linear models for assessing risk models in terms of their hedging effectiveness in the context of the risk-adjusted performance of trading strategies. The article showcases applied Bayesian thinking in the context of financial investment management, highlighting the corresponding concepts of betting and investing, prices and expectations, and coherence and arbitrage-free pricing in futures markets over the period 1990–2008.

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28

Pearl, Lisa, and Sharon Goldwater. Statistical Learning, Inductive Bias, and Bayesian Inference in Language Acquisition. Edited by Jeffrey L. Lidz, William Snyder, and Joe Pater. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199601264.013.28.

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Bayesian models of language acquisition are powerful tools for exploring how linguistic generalizations can be made. Notably, Bayesian models assume children leverage statistical information in sophisticated ways, and so it is important to demonstrate that children’s behavior is consistent with both the assumptions of the Bayesian framework and the predictions of specific models. We first provide a historical overview of behavioral evidence suggesting children utilize available statistical information to make useful generalizations in a variety of tasks. We then discuss the Bayesian modeling framework, including benefits of particular interest to both developmental and theoretical linguists. We conclude with a review of several case studies that demonstrate how Bayesian models can be applied to problems of interest in language acquisition.

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29

Congdon, Peter D. Bayesian Hierarchical Models. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429113352.

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30

Rao, C. R., and Dipak K. Dey. Essential Bayesian Models. Elsevier Science & Technology Books, 2010.

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31

Congdon, P. Bayesian Hierarchical Models. Taylor & Francis Group, 2021.

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32

Barber, David, Silvia Chiappa, and A. Taylan Cemgil. Bayesian Time Series Models. Cambridge University Press, 2012.

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33

Yu, Angela J. Bayesian Models of Attention. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.025.

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Traditionally, attentional selection has been thought of as arising naturally from resource limitations, with a focus on what might be the most apt metaphor, e.g. whether it is a ‘bottleneck’ or ‘spotlight’. However, these simple metaphors cannot account for the specificity, flexibility, and heterogeneity of the way attentional selection manifests itself in different behavioural contexts. A recent body of theoretical work has taken a different approach, focusing on the computational needs of selective processing, relative to environmental constraints and behavioural goals. They typically adopt a normative computational framework, incorporating Bayes-optimal algorithms for information processing and action selection. This chapter reviews some of this recent modelling work, specifically in the context of attention for learning, covert spatial attention, and overt spatial attention.

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34

Barber, David, Silvia Chiappa, and A. Taylan Cemgil. Bayesian Time Series Models. Cambridge University Press, 2011.

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35

Barber, David, Silvia Chiappa, and A. Taylan Cemgil. Bayesian Time Series Models. Cambridge University Press, 2011.

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36

Quintana, José Mario. Multivariate Bayesian forecasting models. 1987.

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37

M, Patrick, Olivier Pourret, and Bruce Marcot. Bayesian Networks. Wiley & Sons, Incorporated, John, 2008.

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38

Hooten, Mevin B., and Trevor Hefley. Bringing Bayesian Models to Life. Taylor & Francis Group, 2021.

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39

McCarley, Jason S., and Aaron S. Benjamin. Bayesian and Signal Detection Models. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199757183.013.0032.

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40

Kruschke, John K., and Wolf Vanpaemel. Bayesian Estimation in Hierarchical Models. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.13.

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Bayesian data analysis involves describing data by meaningful mathematical models, and allocating credibility to parameter values that are consistent with the data and with prior knowledge. The Bayesian approach is ideally suited for constructing hierarchical models, which are useful for data structures with multiple levels, such as data from individuals who are members of groups which in turn are in higher-level organizations. Hierarchical models have parameters that meaningfully describe the data at their multiple levels and connect information within and across levels. Bayesian methods are very flexible and straightforward for estimating parameters of complex hierarchical models (and simpler models too). We provide an introduction to the ideas of hierarchical models and to the Bayesian estimation of their parameters, illustrated with two extended examples. One example considers baseball batting averages of individual players grouped by fielding position. A second example uses a hierarchical extension of a cognitive process model to examine individual differences in attention allocation of people who have eating disorders. We conclude by discussing Bayesian model comparison as a case of hierarchical modeling.

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41

Feldman, Jacob. Bayesian Models of Perceptual Organization. Edited by Johan Wagemans. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199686858.013.007.

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42

Bayesian Forecasting and Dynamic Models. Springer-Verlag, 1997. http://dx.doi.org/10.1007/b98971.

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43

Hooten, Mevin B., and Trevor J. Hefley. Bringing Bayesian Models to Life. Taylor & Francis Group, 2019.

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44

Simon, Christophe, and Philippe Weber. Benefits of Bayesian Network Models. Wiley & Sons, Incorporated, John, 2016.

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45

Simon, Christophe, and Philippe Weber. Benefits of Bayesian Network Models. Wiley & Sons, Incorporated, John, 2016.

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46

Congdon, Peter. Bayesian Models for Categorical Data. Wiley & Sons, Incorporated, John, 2005.

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47

Simon, Christophe, and Philippe Weber. Benefits of Bayesian Network Models. Wiley & Sons, Incorporated, John, 2016.

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48

Hooten, Mevin B., and Trevor J. Hefley. Bringing Bayesian Models to Life. Taylor & Francis Group, 2019.

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49

Hooten, Mevin B., and Trevor Hefley. Bringing Bayesian Models to Life. Taylor & Francis Group, 2019.

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50

Hooten, Mevin B., and Trevor J. Hefley. Bringing Bayesian Models to Life. Taylor & Francis Group, 2019.

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