This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared. Given the interdisciplinary nature of the topics and a moderate prerequisite for the reader, this book should be of interest to a broad audience of quantitative researchers such as computational biologists, computer scientists, econometricians, engineers, probabilists, and statisticians. It can also be used as a textbook for a graduate-level course on Monte Carlo methods.
Jun S. Liu
Most Helpful Customer Reviews
Terse, but precise and very comprehensive. Excellent book for a practicing MC'er
I purchased this book because it had a section on Hybrid Monte Carlo and I was looking for something longer and more recent than Radford Neal's well-known paper on the subject.
The HMC portion of the book was pretty thorough but riddled with spelling and grammatical errors in the text, enough that I became suspicious that this was a draft that had seen only one or two casual QA/editing passes. That led me to suspect there might be errors in the math, which drive me crazy -- nothing frustrates me more than spending a lot of time trying to understand a set of equations before realizing there's a typo in them.
If you're looking for a book that covers a topic that others don't, it's probably worth getting, especially if you're really comfortable with the math, but I wouldn't recommend as a general introduction to Monte Carlo methods.
great book by great mind to great ideas with great math plus great english and great contents given great methods
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