I’ve just received my copy of Advanced Markov Chain Monte Carlo Methods, by Liang, Liu, & Carroll. Although my PhD didn’t really involve any Bayesian methodology (and my undergrad was devoid of any Bayesian influence), I’ve found that the sort of problems I’m now tackling in systems biology demand a Bayesian/MCMC approach. There are a number of introductory books on MCMC, but not that many on advanced techniques.

This book suggests that it could be used as a possible textbook or reference guide in a one-semester statistics graduate course. I’m not entirely convinced that it would be a good textbook, but as a reference it looks very promising. A word of warning, if you’re not familiar with MCMC then you should try the Dani Gamerman MCMC book first. Some later chapters look particularly interesting, including topics on adaptive proposals, population-based MCMC methods and dynamic weightings.

Anyway, I intend to work through the book (well at least a few of the chapters) and post my results/code as I go. Well that’s the plan anyway.

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Interesting! I did not know this book was already out. Looking forward your posts on it!

Comment by xi'an — November 27, 2010 @ 8:08 pm

[...] I mentioned in a recent post, I’ve just received a copy of Advanced Markov Chain Monte Carlo Methods. Chapter 1.4 in the [...]

Pingback by Random variable generation (Pt 1 of 3) « Why? — November 28, 2010 @ 7:35 pm

“[...] if youâ€™re not familiar with MCMC then you should try the Dani Gamerman MCMC book first”

Same as you I had zero Bayesian influence during my undergrad years and my PhD didn’t consider Bayesian-thinking nor MCMC methods. So when I started having the need to go beyond pure likelihood-based approaches I suddenly found the need to fill the gap on MCMC methodology. At that point (3 years ago) THE reference I was pointed to was the Gamerman book. And I am sorry to say that it was a complete lost of time. I found it very badly written and absolutely not the first reference I would pick up should I ever suggest a monography on Monte Carlo (Markov Chain) methods. The fact that, up to a few years ago, it was one of the few books containing simultaneously the words “Monte Carlo” and “Markov Chain” made it a natural reference to suggest. But, oh boy! I found really hard to start understanding MCMC with it. Much better the classic ( and older) monography by Gilks-Richardson-Spiegelhalter or the very new (amazing IMHO) “Handbook of Markov Chain Monte Carlo” edited by Brooks-Gelman-Jones-Li Meng (2011).

Comment by Umberto — December 5, 2011 @ 9:51 am