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Books on the topic 'Markov chain'

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Published: 4 June 2021
Last updated: 7 September 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

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

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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

Y, Saad, Stewart William J. 1946-, and Research Institute for Advanced Computer Science (U.S.), eds. Numerical methods in Markov chain modeling. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1989.

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5

Banisch, Sven. Markov Chain Aggregation for Agent-Based Models. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24877-6.

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6

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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7

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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8

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

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9

Ramlau-Hansen, Henrik. Hattendorff's theorem: A Markov chain and counting process approach. Copenhagen: Laboratory of Actuarial Mathematics, University of Copenhagen, 1987.

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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

Saad, Y. Projection methods for the numerical solution of Markov chain models. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1989.

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12

Banjevic, Dragan. Recurrent relations for distribution of waiting time in Markov chain. [Toronto]: University of Toronto, Department of Statistics, 1994.

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13

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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14

Sinclair, Alistair. Algorithms for Random Generation and Counting: A Markov Chain Approach. Boston, MA: Birkhäuser Boston, 1993. http://dx.doi.org/10.1007/978-1-4612-0323-0.

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15

Sinclair, Alistair. Algorithms for Random Generation and Counting: A Markov Chain Approach. Boston, MA: Birkhäuser Boston, 1993.

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16

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

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17

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

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18

Nir, Abraham. Finite horizon control limits for a partially observable Markov chain. Tel Aviv: Tel Aviv University, Faculty of Management, Leon Recanati Graduate School of Business Administration, 1988.

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19

Jerrum, Mark. Uniform sampling modulo a group of symmetries using Markov chain simulation. Edinburgh: LFCS, Dept. of Computer Science, University of Edinburgh, 1993.

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20

Vijayamohanan, Pillai N. Causality and error correction in Markov chain: Inflation in India revisited. Thiruvananthapuram: Centre for Development Studies, 2004.

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21

Finlon, Matthew A. Analysis of the field artillery battalion organization using a Markov chain. Monterey, Calif: Naval Postgraduate School, 1991.

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22

Argo, Harry M. Analysis of a Lance missile platoon using a semi-Markov chain. Monterey, Calif: Naval Postgraduate School, 1989.

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23

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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24

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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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

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

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27

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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28

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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29

The Markov chain. Bristol: Shearsman Books, 2017.

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30

Gamerman, Dani, and Hedibert F. Lopes. Markov Chain Monte Carlo. Chapman and Hall/CRC, 2006. http://dx.doi.org/10.1201/9781482296426.

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31

Bernadette H.; Arnold E. Perham Perham. Markov Chain Theory Teacher's Guide. Addison-Wesley Publishing Co., 1993.

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32

Brooks, Steve, Andrew Gelman, Galin Jones, and Xiao-Li Meng, eds. Handbook of Markov Chain Monte Carlo. Chapman and Hall/CRC, 2011. http://dx.doi.org/10.1201/b10905.

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33

Gilks, W. R., S. Richardson, and David Spiegelhalter, eds. Markov Chain Monte Carlo in Practice. Chapman and Hall/CRC, 1995. http://dx.doi.org/10.1201/b14835.

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34

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

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35

Brooks, Steve, Andrew Gelman, Xiao-Li Meng, and Galin L. Jones. Handbook of Markov Chain Monte Carlo. Taylor & Francis Group, 2011.

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36

Markov Chain Process: Theory and Cases. Bentham Science Publishers, 2023.

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37

Markov Chain Process: Theory and Cases. Bentham Science Publishers, 2023.

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38

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

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39

Plehn, Thomas. McMc-Methoden - Markov Chain Monte Carlo. GRIN Verlag GmbH, 2009.

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40

Brooks, Steve, Andrew Gelman, Xiao-Li Meng, and Galin Jones. Handbook of Markov Chain Monte Carlo. Taylor & Francis Group, 2011.

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41

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

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42

Brooks, Steve, Andrew Gelman, Xiao-Li Meng, and Galin Jones. Handbook of Markov Chain Monte Carlo. Taylor & Francis Group, 2011.

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43

Keilson, J. Markov Chain Models -- Rarity and Exponentiality. Springer London, Limited, 2012.

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44

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

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45

Markov Chain Process: Theory and Cases. Bentham Science Publishers, 2023.

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46

Banisch, Sven. Markov Chain Aggregation for Agent-Based Models. Springer, 2016.

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47

Banisch, Sven. Markov Chain Aggregation for Agent-Based Models. Springer International Publishing AG, 2018.

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48

Banisch, Sven. Markov Chain Aggregation for Agent-Based Models. Springer London, Limited, 2015.

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49

Coolen, A. C. C., A. Annibale, and E. S. Roberts. Markov Chain Monte Carlo sampling of graphs. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0006.

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Abstract:
This chapter looks at Markov Chain Monte Carlo techniques to generate hard- and soft-constrained exponential random graph ensembles. The essence is to define a Markov chain based on ergodic randomization moves acting on a network with transition probabilities which satisfy detailed balance. This is sufficient to ensure that the Markov chain will sample from the ensemble with the desired probabilities. This chapter studies several commonly seen randomization move sets and carefully defines acceptance probabilities for a range of different ensembles using both the Metropolis–Hastings and the Glauber prescription. Particular care is paid to describe and avoid the pitfalls that can occur in defining randomization moves for hard-constrained ensembles, and applying them without introducing inadvertent bias (i.e. defining and comparing protocols including switch-and-hold and mobility).
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50

Bielelcki, Tomasz R., Stéphane Crépey, and Alexander Herbertsson. Markov Chain Models of Portfolio Credit Risk. Oxford University Press, 2011. http://dx.doi.org/10.1093/oxfordhb/9780199546787.013.0010.

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