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Books on the topic 'Bayesian'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

M, Smith Adrian F., ed. Bayesian theory. Chichester, Eng: Wiley, 1994.

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2

Kamenica, Emir. Bayesian persuasion. Cambridge, MA: National Bureau of Economic Research, 2009.

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3

Inc, ebrary, ed. Bayesian econometrics. Bingley: Emerald JAI, 2008.

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4

Bazett, Trefor. Bayesian Inference. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95792-6.

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5

Zenker, Frank, ed. Bayesian Argumentation. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5357-0.

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6

van Oijen, Marcel. Bayesian Compendium. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55897-0.

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7

Harney, Hanns Ludwig. Bayesian Inference. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41644-1.

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8

Hjort, Nils Lid, Chris Holmes, Peter Muller, and Stephen G. Walker, eds. Bayesian Nonparametrics. Cambridge: Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511802478.

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9

Hamada, Michael S., Alyson G. Wilson, C. Shane Reese, and Harry F. Martz. Bayesian Reliability. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-77950-8.

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10

Harney, Hanns L. Bayesian Inference. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-06006-3.

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11

Lesaffre, Emmanuel, and Andrew B. Lawson. Bayesian Biostatistics. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781119942412.

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12

Bernardo, Jos M., and Adrian F. M. Smith, eds. Bayesian Theory. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1994. http://dx.doi.org/10.1002/9780470316870.

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13

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

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14

Research Institute for Advanced Computer Science (U.S.), ed. Bayesian learning. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1989.

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15

Guttman, Irwin. Bayesian power. Toronto: University of Toronto, Dept. of Statistics, 1986.

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16

Kamenica, Emir. Bayesian persuasion. Cambridge, MA: National Bureau of Economic Research, 2009.

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17

A, Berry Donald, and Stangl Dalene K. 1956-, eds. Bayesian biostatistics. New York: M. Dekker, 1996.

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18

1955-, Hamada Michael, ed. Bayesian reliability. New York, NY: Springer, 2008.

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19

O, Berger James, ed. Bayesian analysis. Amsterdam: North-Holland, 1994.

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20

Bayesian nonparametrics. Cambridge, UK: Cambridge University Press, 2010.

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21

Palfrey, Thomas R. Bayesian implementation. Chur, Switzerland: Harwood Academic Publishers, 1993.

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22

Bayesian econometrics. Chichester: J. Wiley, 2003.

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23

Geweke, John, Gary Koop, and Herman Van Dijk, eds. The Oxford Handbook of Bayesian Econometrics. Oxford University Press, 2011. http://dx.doi.org/10.1093/oxfordhb/9780199559084.001.0001.

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Bayesian econometric methods have enjoyed an increase in popularity in recent years. Econometricians, empirical economists, and policymakers are increasingly making use of Bayesian methods. The Oxford Handbook of Bayesian Econometrics is a single source about Bayesian methods in specialized fields. It contains articles by leading Bayesians on the latest developments in their specific fields of expertise. The volume provides broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing. It reviews the state of the art in Bayesian econometric methodology, with articles on posterior simulation and Markov chain Monte Carlo methods, Bayesian nonparametric techniques, and the specialized tools used by Bayesian time series econometricians such as state space models and particle filtering. It also includes articles on Bayesian principles and methodology.

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24

Nyberg, Svein. Bayesian Statistics. Wiley & Sons, Incorporated, John, 2018.

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25

Smith, Adrian F. M., and Jose Bernardo. Bayesian Theory. Wiley & Sons, Incorporated, John, 2030.

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26

Mazer, Emmanuel, Pierre Bessiere, Juan Ahuactzin, and Kamel Mekhnacha. Bayesian Programming. Taylor & Francis Group, 2013.

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27

Palfrey, Thomas R. Bayesian Implementation. Taylor & Francis Group, 2020.

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28

Bessiere, Pierre, Emmanuel Mazer, Juan Ahuactzin, and Kamel Mekhnacha. Bayesian Programming. Chapman and Hall/CRC, 2013. http://dx.doi.org/10.1201/b16111.

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29

Martin, Andrew D. Bayesian Analysis. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0021.

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This article surveys modern Bayesian methods of estimating statistical models. It first provides an introduction to the Bayesian approach for statistical inference, contrasting it with more conventional approaches. It then explains the Monte Carlo principle and reviews commonly used Markov Chain Monte Carlo (MCMC) methods. This is followed by a practical justification for the use of Bayesian methods in the social sciences, and a number of examples from the literature where Bayesian methods have proven useful are shown. The article finally provides a review of modern software for Bayesian inference, and a discussion of the future of Bayesian methods in political science. One area ripe for research is the use of prior information in statistical analyses. Mixture models and those with discrete parameters (such as change point models in the time-series context) are completely underutilized in political science.

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30

Scutari, Marco, and Jean-Baptiste Denis. Bayesian Networks. Chapman and Hall/CRC, 2014. http://dx.doi.org/10.1201/b17065.

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31

Palfrey, T., and S. Srivastave. Bayesian Implementation. Routledge, 2013. http://dx.doi.org/10.4324/9781315014722.

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32

Bovens, Luc, and Stephan Hartmann. Bayesian Epistemology. Oxford University Press, USA, 2004.

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33

Bayesian Econometrics. MDPI, 2020. http://dx.doi.org/10.3390/books978-3-03943-786-3.

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34

Bayesian Inference. Elsevier, 2010. http://dx.doi.org/10.1016/c2009-0-01674-2.

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35

Chib, Siddhartha, William Griffiths, Gary Koop, and Dek Terrell, eds. Bayesian Econometrics. Emerald Group Publishing Limited, 2008. http://dx.doi.org/10.1016/s0731-9053(2008)23.

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36

Asensio Ramos, Andrés, and Íñigo Arregui, eds. Bayesian Astrophysics. Cambridge University Press, 2018. http://dx.doi.org/10.1017/9781316182406.

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37

Berry, Donald A., and Dalene Stangl. Bayesian Biostatistics. CRC Press, 2018. http://dx.doi.org/10.1201/9781315274300.

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38

Gill, Jeff. Bayesian Methods. Chapman and Hall/CRC, 2007. http://dx.doi.org/10.1201/9781420010824.

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39

Gill, Jeff. Bayesian Methods. Chapman and Hall/CRC, 2014. http://dx.doi.org/10.1201/b17888.

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40

Gill, Jeff. Bayesian Methods. Chapman and Hall/CRC, 2002. http://dx.doi.org/10.1201/9781420057478.

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41

Griffiths, Thomas L., Joshua B. Tenenbaum, and Charles Kemp. Bayesian Inference. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199734689.013.0003.

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42

Pourret, Olivier, Patrick Naim, and Bruce Marcot, eds. Bayesian Networks. Wiley, 2008. http://dx.doi.org/10.1002/9780470994559.

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43

Bayesian Nonparametrics. New York: Springer-Verlag, 2003. http://dx.doi.org/10.1007/b97842.

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44

Tejedor, Javier Prieto, ed. Bayesian Inference. InTech, 2017. http://dx.doi.org/10.5772/66264.

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45

Palfrey, T., and S. Srivastave. Bayesian Implementation. Taylor & Francis Group, 2013.

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46

Lawson, Andrew B., and Emmanuel Lesaffre. Bayesian Biostatistics. Wiley & Sons, Incorporated, John, 2012.

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47

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

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48

Müller, Peter, Stephen G. Walker, Nils Lid Hjort, and Chris Holmes. Bayesian Nonparametrics. Cambridge University Press, 2011.

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49

Doya, Kenji, Shin Ishii, Alexandre Pouget, and Rajesh P. N. Rao, eds. Bayesian Brain. The MIT Press, 2006. http://dx.doi.org/10.7551/mitpress/9780262042383.001.0001.

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50

Premchaiswadi, Wichian, ed. Bayesian Networks. InTech, 2012. http://dx.doi.org/10.5772/2551.

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