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Notes
- 1.
MCMC is well covered in several text books. Mackay (2003) gives a thorough and readable introduction to MCMC and Gibbs Sampling. Russell and Norvig (2009) explain MCMC in the context of approximate inference for Bayesian networks. Hastie et al. (2009) also give a more technical account of sampling from the posterior. Andrieu et al. (2003) Machine Learning paper gives a thorough introduction to MCMC for Machine Learning. There are also some excellent tutorials on the web including Walsh (2004) and Iain Murray’s video tutorial (Murray 2009) for machine learning summer school.
Recommended Reading
MCMC is well covered in several text books. Mackay (2003) gives a thorough and readable introduction to MCMC and Gibbs Sampling. Russell and Norvig (2009) explain MCMC in the context of approximate inference for Bayesian networks. Hastie et al. (2009) also give a more technical account of sampling from the posterior. Andrieu et al. (2003) Machine Learning paper gives a thorough introduction to MCMC for Machine Learning. There are also some excellent tutorials on the web including Walsh (2004) and Iain Murray’s video tutorial (Murray 2009) for machine learning summer school.
Andrieu C, DeFreitas N, Doucet A, Jordan MI (2003) An introduction to MCMC for machine learning. Mach Learn 50(1):5–43
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference and perception, 2nd edn. Springer, New York
Hastings WK (1970) Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57:97–109
Mackay DJC (2003) Information theory, inference and learning algorithms. Cambridge University Press, Cambridge
Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller A, Teller H (1953) Equations of state calculations by fast computing machines. J Chem Phys 21:1087–1091
Metropolis N, Ulam S (1949) The Monte Carlo method. J Am Stat Assoc 44(247):335–341
Murray I (2009) Markov chain Monte Carlo. http://videolectures.net/mlss09uk_murray_mcmc/. Retrieved 25 July 2010
Russell S, Norvig P (2009) Artificial intelligence: a modern approach, 3rd edn. Prentice Hall, Englewood Cliffs
Walsh B (2004) Markov chain Monte Carlo and Gibbs sampling. http://nitro.biosci.arizona.edu/courses/EEB581-2004/handouts/Gibbs. Retrieved 25 July 2010
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Sammut, C. (2017). Markov Chain Monte Carlo. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_952
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