Any suggestions for a good source to learn MCMC methods?
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3$\begingroup$ Related question: good summaries (reviews, books) on various applications of Markov chain Monte Carlo (MCMC) $\endgroup$– SilverfishCommented Jan 16, 2015 at 12:20
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$\begingroup$ Nice coverage of sampling methods is in Bishop microsoft.com/en-us/research/uploads/prod/2006/01/… and MacKay inference.org.uk/itprnn/book.pdf $\endgroup$– NeuroPandaCommented Dec 1, 2023 at 21:03
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7 Answers
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For online tutorials, there are
- A tutorial in MCMC, by Sahut (2000)
- Tutorial on Markov Chain Monte Carlo, by Hanson (2000)
- Markov Chain Monte Carlo for Computer Vision, by Zhu et al. (2005)
- Introduction to Markov Chain Monte Carlo simulations and their statistical analysis, by Berg (2004).
- A Tutorial on Markov Chain Monte-Carlo and Bayesian Modeling by Martin B. Haugh (2021).
Practical Markov Chain Monte Carlo, by Geyer (Stat. Science, 1992), is also a good starting point, and you can look at the MCMCpack or mcmc R packages for illustrations.
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$\begingroup$ fyi : 2nd two links in the list are broken $\endgroup$ Commented Dec 8, 2015 at 21:23
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I haven't read it (yet), but if you're into R, there is Christian P. Robert's and George Casella's book: Introducing Monte Carlo Methods with R (Use R)
I know of it from following his (very good) blog
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$\begingroup$ This book doesn't go in depth into MCMC. It actually have a page on it and skip MCMC theory in its entirety to go into Metropolist hashing. $\endgroup$ Commented Apr 19, 2017 at 5:57
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$\begingroup$ While you are 100% entitled to your own opinion on our book, I beg to disagree with the notion that we do not go in depth into MCMC there. Judging from the question I do not think the OP was asking for the deep theory of MCMC algorithms (which is somehow covered in our earlier book). $\endgroup$– Xi'anCommented Nov 1, 2017 at 11:27
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Gilks W.R., Richardson S., Spiegelhalter D.J. Markov Chain Monte Carlo in Practice. Chapman & Hall/CRC, 1996.
A relative oldie now, but still a goodie.
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Chapter 4, 'Inference from simulations and monitoring convergence' by Gelman and Shirley, is available online.
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1$\begingroup$ Looks set to kick Gilks, Richardson & Spiegelhalter (1996) into the long grass when that comes it in May. $\endgroup$– onestopCommented Jan 3, 2011 at 9:35
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-- a more recently updated book than Gilks, Richardson & Spiegelhalter. I haven't read it myself, but it was well reviewed in Technometrics in 2008, and the first edition also got a good review in The Statistician back in 1998.
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Another classic position (as accompanied to already mentioned Introducing Monte Carlo Methods with R):
Monte Carlo Statistical Methods by Robert and Casella (2004)
in the Use R! series there is also:
Introduction to Probability Simulation and Gibbs Sampling with R by Suess and Trumbo (2010)
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The text I have found most accessible is Bayesian Cognitive Modeling: A Practical Course. Very clear exposition. The book has great examples in BUGS, and they have been ported to Stan on its github examples page.