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Books on the topic 'Bayesian models'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

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

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2

Quintana, Jose Mario. Multivariate Bayesian forecasting models. [s.l.]: typescript, 1987.

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3

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

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4

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

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5

Hooten, Mevin B., and Trevor J. Hefley. Bringing Bayesian Models to Life. Boca Raton, FL : CRC Press, Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429243653.

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6

Young, Simon Christopher. Bayesian models and repeated games. [s.l.]: typescript, 1989.

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7

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

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8

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

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9

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

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10

Bayesian analysis of linear models. New York: M. Dekker, 1985.

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11

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

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12

West, Mike. Bayesian forecasting and dynamic models. New York: Springer, 1989.

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13

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

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14

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

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15

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

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16

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

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17

Bauwens, Luc. Bayesian inference in dynamic econometric models. Oxford [England]: Oxford University Press, 1999.

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18

M, Wiper Mike, and Ríos Insua David 1964-, eds. Bayesian analysis of stochastic process models. Hoboken, New Jersey: 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. New York: Marcel Dekker, 2000.

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20

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

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21

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

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22

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

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23

Nonlinear financial econometrics: Forecasting models, computational and Bayesian models. Basingstoke: Palgrave Macmillan, 2011.

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24

1956-, Allenby Greg M., and McCulloch Robert E, eds. Bayesian statistics and marketing. Hoboken, NJ: Wiley, 2005.

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25

Dr, Hartmann Stephan, ed. Bayesian epistemology. Oxford: Clarendon, 2003.

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26

Rossi, Peter E. Bayesian statistics and marketing. Chichester, UK: Wiley, 2005.

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27

Benedek, Csaba. Multi-Level Bayesian Models for Environment Perception. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83654-2.

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28

Cardoso, M. Jorge, Ivor Simpson, Tal Arbel, Doina Precup, and Annemie Ribbens, eds. Bayesian and grAphical Models for Biomedical Imaging. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12289-2.

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29

Kenny, Geoff. Bayesian VAR models for forecasting Irish inflation. Dublin: Central Bank of Ireland, Economic Analysis, Research and Publications Department, 1998.

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30

Ankush, Mittal, and Kassim Ashraf, eds. Bayesian network technologies: Applications and graphical models. Hershey, PA: Idea Group Pub., 2007.

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31

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

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32

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

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33

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

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34

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

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35

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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36

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

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37

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

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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. New York: 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

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

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47

Bayesian Models for Categorical Data. Wiley, 2005.

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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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