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Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples Hardcover – August 23, 2010

ISBN-13: 978-0470748268 ISBN-10: 0470748265 Edition: 1st

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Product Details

  • Hardcover: 378 pages
  • Publisher: Wiley; 1 edition (August 23, 2010)
  • Language: English
  • ISBN-10: 0470748265
  • ISBN-13: 978-0470748268
  • Product Dimensions: 6.4 x 1 x 9.2 inches
  • Shipping Weight: 1.5 pounds (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Best Sellers Rank: #2,438,083 in Books (See Top 100 in Books)

Editorial Reviews

Review

“The book is suitable as a textbook for one-semestercourses on Monte Carlo methods, offered at the advance postgraduatelevels.”  (Mathematical Reviews, 1 December2012)

"Researchers working in the field of applied statistics will profitfrom this easy-to-access presentation. Further illustration is doneby discussing interesting examples and relevant applications. Thevaluable reference list includes technical reports which are hardto and by searching in public data bases." (Zentralblatt MATH,2011)

"This book can be used as a textbook or a reference book for aone-semester graduate course in statistics, computational biology,engineering, and computer sciences. Applied or theoreticalresearchers will also find this book beneficial." (Breitbart.com:Business Wire , 1 February 2011)

 

"The Markov Chain Monte Carlo methodhas now become the dominant methodology for solving many classes ofcomputational problems in science and technology." (SciTech BookNews, December 2010)

From the Back Cover

Markov Chain Monte Carlo (MCMC) methods are now an indispensabletool in scientific computing. This book discusses recentdevelopments of MCMC methods with an emphasis on those making useof past sample information during simulations. The applicationexamples are drawn from diverse fields such as bioinformatics,machine learning, social science, combinatorial optimization, andcomputational physics.

Key Features:

  • Expanded coverage of the stochastic approximation Monte Carloand dynamic weighting algorithms that are essentially immune tolocal trap problems.
  • A detailed discussion of the Monte Carlo Metropolis-Hastingsalgorithm that can be used for sampling from distributions withintractable normalizing constants.
  • Up-to-date accounts of recent developments of the Gibbssampler.
  • Comprehensive overviews of the population-based MCMC algorithmsand the MCMC algorithms with adaptive proposals.
  • Accompanied by a supporting website featuring datasets used inthe book, along with codes used for some simulation examples.

This book can be used as a textbook or a reference book for aone-semester graduate course in statistics, computational biology,engineering, and computer sciences. Applied or theoreticalresearchers will also find this book beneficial.

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5 of 5 people found the following review helpful By The Statistician on August 10, 2011
Format: Hardcover
When I found this book I was excited to see that there is a book in 2010 about MCMC sampling. But I ended up looking up the referred papers and reading the original work. The problem stems from fact that most of work in the field of sampling has been done in Particle Physics and the authors do not spend enough text on explaining the motivations and details of the algorithm and the shortcomings that led to invention of the next algorithm. The authors also devote a lot of space in the book for description of their own work and do not give a fair overview of the field.
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2 of 2 people found the following review helpful By astro on October 15, 2012
Format: Hardcover
I just got this book because I wanted to know more about a few specific MCMC samplers which were listed in the Table of Contents. I had it for about 5 minutes before I was already coding up my own versions. So far, the book seems to be a very good resource for someone interested in MCMC techniques as it provides the step-by-step algorithm to implement many of the exotic samplers. It does have problem sets, so it seems to be a text book. I'm not sure how useful it will be in that regard since it doesn't look to do much theory or comparisons between the samplers and different problems. Maybe I'll know more soon....
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