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Books on the topic 'Markov decision theory'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Chang, Hyeong Soo. Simulation-Based Algorithms for Markov Decision Processes. 2nd ed. London: Springer London, 2013.

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2

Ulrich, Rieder, and SpringerLink (Online service), eds. Markov Decision Processes with Applications to Finance. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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3

Koole, G. M. Monotonicity in Markov reward and decision chains: Theory and applications. Boston: Now Publishers, 2007.

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4

Feinberg, Eugene A. Handbook of Markov Decision Processes: Methods and Applications. Boston, MA: Springer US, 2002.

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5

Ching, Wai-Ki. Markov Chains: Models, Algorithms and Applications. 2nd ed. Boston, MA: Springer US, 2013.

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6

Markov chain Monte Carlo: Stochastic simulation for Bayesian inference. London: Chapman & Hall, 1997.

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7

Gamerman, Dani. Markov chain Monte Carlo: Stochastic simulation for Bayesian inference. London: Chapman & Hall, 1997.

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8

Rachev, Svetlozar T. Bayesian Methods in Finance. New York: John Wiley & Sons, Ltd., 2008.

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9

T, Rachev S., ed. Bayesian methods in finance. Hoboken, N.J: Wiley, 2008.

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10

Freitas, Lopes Hedibert, ed. Markov chain Monte Carlo: Stochastic simulation for Bayesian inference. 2nd ed. Boca Raton: Taylor & Francis, 2006.

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11

Gibbs, Alison. Bounding convergence time of the Gibbs sampler in Bayesian image restoration. Toronto: University of Toronto, Dept. of Statistics, 1998.

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12

1947-, Gianola Daniel, ed. Likelihood, Bayesian and MCMC methods in quantitative genetics. New York: Springer-Verlag, 2002.

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13

1955-, Lucas Peter, Gámez José A, and Salmerón Antonio, eds. Advances in probabilistic graphical models. Berlin: Springer, 2007.

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14

Petrescu, Ion. Psihosociologia eficienței economice. Bucureși: Editura Academiei Române, 1991.

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15

Continuoustime Markov Decision Processes Theory And Applications. Springer, 2009.

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16

Chang, Hyeong Soo, Michael C. Fu, and Jiaqiao Hu. Simulation-Based Algorithms for Markov Decision Processes. Springer, 2013.

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17

Simulation-Based Algorithms for Markov Decision Processes. Springer London, Limited, 2013.

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18

Chang, Hyeong Soo, Michael C. Fu, and Jiaqiao Hu. Simulation-Based Algorithms for Markov Decision Processes. Springer London, Limited, 2015.

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19

Chang, Hyeong Soo, Michael C. Fu, Jiaqiao Hu, and Steven I. Marcus. Simulation-Based Algorithms for Markov Decision Processes. Springer London, Limited, 2010.

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20

Hernández-Lerma, Onésimo, and Xianping Guo. Continuous-Time Markov Decision Processes: Theory and Applications. Springer, 2012.

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21

Hernandez-Lerma, Onesimo, and Xianping Guo. Continuous-Time Markov Decision Processes: Theory and Applications. Springer, 2010.

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22

Bäuerle, Nicole, and Ulrich Rieder. Markov Decision Processes with Applications to Finance. Springer, 2011.

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23

Yue, Wuyi, and Qiying Hu. Markov Decision Processes with Their Applications. Springer, 2010.

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24

Fink, Gernot A. A. Markov Models for Pattern Recognition: From Theory to Applications. Springer, 2016.

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25

Markov Models For Pattern Recognition From Theory To Applications. Springer London Ltd, 2014.

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26

Frühwirth-Schnatter, Sylvia. Finite Mixture and Markov Switching Models. Springer New York, 2010.

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27

Finite Mixture and Markov Switching Models. Springer, 2006.

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28

Ng, Michael K., Wai-Ki Ching, Ximin Huang, and Tak-Kuen Siu. Markov Chains: Models, Algorithms and Applications. Springer, 2013.

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29

Ng, Michael K., and Wai-Ki Ching. Markov Chains: Models, Algorithms and Applications. Springer, 2010.

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30

Ng, Michael K., and Wai-Ki Ching. Markov Chains: Models, Algorithms and Applications. Springer, 2006.

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31

Ng, Michael K., Wai-Ki Ching, Ximin Huang, and Tak-Kuen Siu. Markov Chains: Models, Algorithms and Applications. Springer, 2015.

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32

Koole, Ger. Monotonicity in Markov Reward and Decision Chains: Theory and Applications (Foundations and Trends in Stochastic Systems). Now Publishers Inc, 2007.

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33

S, Kendall W., Liang F. 1970-, and Wang J. S. 1960-, eds. Markov chain Monte Carlo: Innovations and applications. Singapore: World Scientific, 2005.

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34

Markov chain Monte Carlo: Innovations and applications. Singapore: World Scientific, 2006.

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35

Hernandez-Lerma, Onesimo, and Xianping Guo. Continuous-Time Markov Decision Processes: Theory and Applications (Stochastic Modelling and Applied Probability Book 62). Springer, 2009.

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36

Yue, Wuyi, and Qiying Hu. Markov Decision Processes with Their Applications (Advances in Mechanics and Mathematics). Springer, 2007.

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37

Fabozzi, Frank J., Svetlozar T. Rachev, John S. J. Hsu, and Biliana S. Bagasheva. Bayesian Methods in Finance (Frank J. Fabozzi Series). Wiley, 2008.

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38

Lopes, Hedibert F., and Dani Gamerman. Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition. Taylor & Francis Group, 2006.

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39

Markov Chain Monte Carlo: Innovations And Applications (Lecture Notes Series, Institute for Mathematical Sciences, N) (Lecture Note). World Scientific Publishing Company, 2005.

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40

Rodriguez, Abel, and Athanasios Kottas. Bayesian Nonparametric Mixture Models: Methods and Applications. Taylor & Francis Group, 2023.

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41

Tatarinova, Tatiana V., and Alan Schumitzky. Nonlinear Mixture Models: A Bayesian Approach. Imperial College Press, 2015.

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42

Lopes, Hedibert F., and Dani Gamerman. Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition. Taylor & Francis Group, 2006.

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43

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

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44

Congdon, Peter. Bayesian Models for Categorical Data. Wiley & Sons, Incorporated, John, 2007.

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45

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

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46

Congdon, Peter. Bayesian Models for Categorical Data. Wiley & Sons Australia, Limited, John, 2006.

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47

Bayesian Models for Categorical Data. Wiley, 2005.

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48

Sorensen, Daniel, and Daniel Gianola. Likelihood, Bayesian and MCMC Methods in Quantitative Genetics. Springer, 2007.

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49

Lucas, Peter, José A. Gámez, and Antonio Salmerón Cerdan. Advances in Probabilistic Graphical Models. Springer London, Limited, 2007.

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50

Lucas, Peter, José A. Gámez, Various, and Antonio Salmerón Cerdan. Advances in Probabilistic Graphical Models. Springer, 2010.

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