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Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples [Hardcover]

by Faming Liang, Chuanhai Liu, Raymond Carroll
4.0 out of 5 stars  See all reviews (2 customer reviews)

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Book Description

August 23, 2010 0470748265 978-0470748268 1
Markov Chain Monte Carlo (MCMC) methods are now an indispensable tool in scientific computing. This book discusses recent developments of MCMC methods with an emphasis on those making use of past sample information during simulations. The application examples are drawn from diverse fields such as bioinformatics, machine learning, social science, combinatorial optimization, and computational physics.

Key Features:

  • Expanded coverage of the stochastic approximation Monte Carlo and dynamic weighting algorithms that are essentially immune to local trap problems.
  • A detailed discussion of the Monte Carlo Metropolis-Hastings algorithm that can be used for sampling from distributions with intractable normalizing constants.
  • Up-to-date accounts of recent developments of the Gibbs sampler.
  • Comprehensive overviews of the population-based MCMC algorithms and the MCMC algorithms with adaptive proposals.

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


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Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples + Handbook of Markov Chain Monte Carlo (Chapman & Hall/CRC Handbooks of Modern Statistical Methods)
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Editorial Reviews

Review

“The book is suitable as a textbook for one-semester courses on Monte Carlo methods, offered at the advance postgraduate levels.”  (Mathematical Reviews, 1 December 2012)

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

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

 

"The Markov Chain Monte Carlo method has now become the dominant methodology for solving many classes of computational problems in science and technology." (SciTech Book News, December 2010)

From the Back Cover

Markov Chain Monte Carlo (MCMC) methods are now an indispensable tool in scientific computing. This book discusses recent developments of MCMC methods with an emphasis on those making use of past sample information during simulations. The application examples are drawn from diverse fields such as bioinformatics, machine learning, social science, combinatorial optimization, and computational physics.

Key Features:

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

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


Product Details

  • Hardcover: 378 pages
  • Publisher: Wiley; 1 edition (August 23, 2010)
  • Language: English
  • ISBN-10: 0470748265
  • ISBN-13: 978-0470748268
  • Product Dimensions: 9.1 x 6.2 x 1 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: #2,020,978 in Books (See Top 100 in Books)

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5 of 5 people found the following review helpful
3.0 out of 5 stars A recent review of the topic 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
5.0 out of 5 stars so far so good October 15, 2012
By astro
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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