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Books on the topic 'Markov chain Monte Carlo methods'

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Published: 4 June 2021
Last updated: 25 May 2024

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1

Liang, Faming, Chuanhai Liu, and Raymond J. Carroll. Advanced Markov Chain Monte Carlo Methods. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470669723.

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2

Handbook for Markov chain Monte Carlo. Boca Raton: Taylor & Francis, 2011.

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3

R, Gilks W., Richardson S, and Spiegelhalter D. J, eds. Markov chain Monte Carlo in practice. Boca Raton, Fla: Chapman & Hall, 1998.

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4

R, Gilks W., Richardson S, and Spiegelhalter D. J, eds. Markov chain Monte Carlo in practice. London: Chapman & Hall, 1996.

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5

Liang, F. Advanced Markov chain Monte Carlo methods: Learning from past samples. Hoboken, NJ: Wiley, 2010.

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6

Joseph, Anosh. Markov Chain Monte Carlo Methods in Quantum Field Theories. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46044-0.

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7

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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8

1946-, Winkler Gerhard, ed. Image analysis, random fields and Markov chain Monte Carlo methods: A mathematical introduction. 2nd ed. Berlin: Springer, 2003.

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9

Gerhard, Winkler. Image analysis, random fields and Markov chain Monte Carlo methods: A mathematical introduction. 2nd ed. Berlin: Springer, 2003.

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10

Winkler, Gerhard. Image Analysis, Random Fields and Markov Chain Monte Carlo Methods. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55760-6.

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11

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

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12

Neal, Radford M. Markov chain Monte Carlo methods based on "slicing" the density function. Toronto: University of Toronto, Dept. of Statistics, 1997.

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13

Roberts, Gareth O. Markov chain Monte Carlo: Some practical implications of theoretical results. Toronto: University of Toronto, 1997.

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14

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

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15

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

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16

Cowles, Mary Kathryn. A simulation approach to convergence rates for Markov chain Monte Carlo algorithms. [Toronto]: University of Toronto, Dept. of Statistics, 1996.

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17

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

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18

Markov chain Monte Carlo simulations and their statistical analysis: With web-based fortran code. Singapore: World Scientific Publishing, 2004.

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19

Markov chain Monte Carlo simulations and their statistical analysis: With web-based Fortran code. Hackensack, NJ: World Scientific, 2004.

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20

Brémaud, Pierre. Markov chains: Gibbs fields, Monte Carlo simulation, and queues. New York: Springer, 1999.

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21

Mikhaĭlov, G. A. New Monte Carlo methods with estimating derivatives. Utrecht, The Netherlands: VSP, 1995.

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22

George, Casella, and SpringerLink (Online service), eds. Introducing Monte Carlo Methods with R. New York, NY: Springer Science+Business Media, LLC, 2010.

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23

Nicol, David M. Automated parallelization of discrete state-space generation. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 2000.

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24

Nicol, David M. Automated parallelization of discrete state-space generation. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 2000.

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25

Suwa, Hidemaro. Geometrically Constructed Markov Chain Monte Carlo Study of Quantum Spin-phonon Complex Systems. Tokyo: Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-54517-0.

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26

Image analysis, random fields, and dynamic Monte Carlo methods: A mathematical introduction. Berlin: Springer-Verlag, 1995.

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27

Lunn, David Jonathan. The application of Markov chain Monte Carlo techniques to the study of population pharmacokinetics. Manchester: University of Manchester, 1995.

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28

Limit Theory for Spatial Processes, Bootstrap Quantile Variance Estimators, and Efficiency Measures for Markov Chain Monte Carlo. [New York, N.Y.?]: [publisher not identified], 2014.

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29

Carroll, Raymond, Faming Liang, and Chuanhai Liu. Advanced Markov Chain Monte Carlo Methods. Wiley & Sons, Incorporated, John, 2010.

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30

Kamatani, Kengo. Stability of Markov Chain Monte Carlo Methods. Springer Japan, 2023.

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31

Richardson, S., David Spiegelhalter, and W. R. Gilks. Markov Chain Monte Carlo in Practice. Taylor & Francis Group, 1995.

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32

Richardson, S., David Spiegelhalter, and W. R. Gilks. Markov Chain Monte Carlo in Practice. Taylor & Francis Group, 1995.

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33

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

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34

Carroll, Raymond, Faming Liang, and Chuanhai Liu. Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples. Wiley & Sons, Incorporated, John, 2011.

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35

Carroll, Raymond, Faming Liang, and Chuanhai Liu. Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples. Wiley & Sons, Limited, John, 2010.

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36

Carroll, Raymond, Faming Liang, and Chuanhai Liu. Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples. Wiley & Sons, Incorporated, John, 2010.

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37

Carroll, Raymond, Faming Liang, and Chuanhai Liu. Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples. Wiley & Sons, Incorporated, John, 2011.

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38

Allen, Michael P., and Dominic J. Tildesley. Monte Carlo methods. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803195.003.0004.

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The estimation of integrals by Monte Carlo sampling is introduced through a simple example. The chapter then explains importance sampling, and the use of the Metropolis and Barker forms of the transition matrix defined in terms of the underlying matrix of the Markov chain. The creation of an appropriately weighted set of states in the canonical ensemble is described in detail and the method is extended to the isothermal–isobaric, grand canonical and semi-grand ensembles. The Monte Carlo simulation of molecular fluids and fluids containing flexible molecules using a reptation algorithm is discussed. The parallel tempering or replica exchange method for more efficient exploration of the phase space is introduced, and recent advances including solute tempering and convective replica exchange algorithms are described.
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39

Boudreau, Joseph F., and Eric S. Swanson. Monte Carlo methods. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0007.

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Monte Carlo methods are those designed to obtain numerical answers with the use of random numbers . This chapter discusses random engines, which provide a pseudo-random pattern of bits, and their use in for sampling a variety of nonuniform distributions, for both continuous and discrete variables. A wide selection of uniform and nonuniform variate generators from the C++ standard library are reviewed, and common techniques for generating custom nonuniform variates are discussed. The chapter presents the uses of Monte Carlo to evaluate integrals, particularly multidimensional integrals, and then introduces the important method of Markov chain Monte Carlo, suitable for solving a wide range of scientific problems that require the sampling of complicated multivariate distributions. Relevant topics in probability and statistics are also introduced in this chapter. Finally, the topics of thermalization, autocorrelation, multimodality, and Gibbs sampling are presented.
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40

Markov Chain Monte Carlo in Practice (Interdisciplinary Statistics). Chapman & Hall/CRC, 1995.

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41

Joseph, Anosh. Markov Chain Monte Carlo Methods in Quantum Field Theories: A Modern Primer. Springer, 2020.

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42

Geometrically Constructed Markov Chain Monte Carlo Study of Quantum Spinphonon Complex Systems Springer Theses. Springer Verlag, Japan, 2013.

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43

Winkler, Gerhard. Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction. Springer London, Limited, 2012.

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44

Winkler, Gerhard. Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction. Springer Berlin / Heidelberg, 2012.

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45

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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46

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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47

Suwa, Hidemaro. Geometrically Constructed Markov Chain Monte Carlo Study of Quantum Spin-Phonon Complex Systems. Springer London, Limited, 2013.

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48

Geometrically Constructed Markov Chain Monte Carlo Study of Quantum Spin-Phonon Complex Systems. Springer Japan, 2016.

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49

Ciric, Aleksandar. A Guide to Monte Carlo and Quantum Monte Carlo methods : Quantum Monte Carlo: Variational and Diffusion; MC in general; Markov Chain; Statistics; Random number generators; Hidden Monte Carlo. Createspace Independent Publishing Platform, 2016.

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50

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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