on March 27, 2000
This book covers a wide range of applications in Statistical Mechanics, with clear explanation, examples, tips, algorithms, and explicit programs at the end of the book. It is good for beginners and experienced alike, since it discusses "classical" and modern algorithms. It is a must for those who want to make actual numerical calculations in Statistical Physics.
on September 21, 2008
This book is clearly written, well-organized and very user-friendly. I highly recommend this book to anyone interested in Monte Carlo methods.
Unlike many other books that focus on its applications, this book spends the first three chapters on a thorough explanation of the mechanism: how Monte Carlo methods work, Markov chain, detailed balance, ergodicity, and on how to measure their efficiency. The book is clear and thorough as it makes sense to an average physics student. However, it may not be so rigorous from a mathematician's viewpoint.
I particular like this book for its down-to-earth style. When the authors talk about an algorithm, they give their self-contained C/C++ code (beautifully written programs), and explain underlying principles, technical details as well as common pitfalls (e.g., random number generators), which are all very helpful.
The topics in this book are clearly physics-based, with a focus on Ising model and other related models. Many important topics are covered: cluster move, renormalization method, entropic sampling, tempering, etc.
However, I kind of feel that the lattice models may not deserve so much attention. One reason the authors choose to do so is perhaps that these models are so simple and so much fun. On the other hand, applications to molecular systems are not mentioned at all (well, most people prefer molecular dynamics instead of Monte Carlo for molecular systems anyway). So I should say that an average person interested in chemistry and biology may prefer some other books, e.g., Frenkel's book. But what you get from this book is a deep and enjoyable understanding, and you may not care about applications too much at the end. I should also mention that the book uses some nice (kind of like plastic) papers, which may feel a little weird at first.
on July 11, 2006
Overall, it's an excellent book on the practice of Monte Carlo and the c++ code in appendix are very instructive (Random Number Generators, Solid Monte Carlo Routines, etc.). It does have certain weaknesses though.
1) Sometimes the description are trivial in principle but written in great details. For example, on Pg 58 on the exact methods (so-called 'efficient way') of calculating averaged quantities from simulation.
2) Most of the content are heuristic. The discussion of the whole book is based on practice, although you do find something looks like a rigious proof (but no in fact). By rigious, I mean the proof should be based on Markov Chain and related properties of random process and statistical physics.
But as I said in the beginning, this is a invaluable book to anyone who wants to use Monte Carlo method in his/her domain. For myself, I am using this book as a reference to tackle functional optimization - Simulated Annealing, which is a very close sibling of Monte Carlo method.