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Markov Chain Monte Carlo in Practice (Chapman & Hall/CRC Interdisciplinary Statistics Series) [Hardcover]

W.R. Gilks , S. Richardson , David Spiegelhalter
5.0 out of 5 stars  See all reviews (2 customer reviews)
Price: £84.00 & FREE Delivery in the UK. Details
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Book Description

1 Jan 1996 Chapman & Hall/CRC Interdisciplinary Statistics Series (Book 2)
In a family study of breast cancer, epidemiologists in Southern California increase the power for detecting a gene-environment interaction. In Gambia, a study helps a vaccination program reduce the incidence of Hepatitis B carriage. Archaeologists in Austria place a Bronze Age site in its true temporal location on the calendar scale. And in France, researchers map a rare disease with relatively little variation.

Each of these studies applied Markov chain Monte Carlo methods to produce more accurate and inclusive results. General state-space Markov chain theory has seen several developments that have made it both more accessible and more powerful to the general statistician. Markov Chain Monte Carlo in Practice introduces MCMC methods and their applications, providing some theoretical background as well. The authors are researchers who have made key contributions in the recent development of MCMC methodology and its application.

Considering the broad audience, the editors emphasize practice rather than theory, keeping the technical content to a minimum. The examples range from the simplest application, Gibbs sampling, to more complex applications. The first chapter contains enough information to allow the reader to start applying MCMC in a basic way. The following chapters cover main issues, important concepts and results, techniques for implementing MCMC, improving its performance, assessing model adequacy, choosing between models, and applications and their domains.

Markov Chain Monte Carlo in Practice is a thorough, clear introduction to the methodology and applications of this simple idea with enormous potential. It shows the importance of MCMC in real applications, such as archaeology, astronomy, biostatistics, genetics, epidemiology, and image analysis, and provides an excellent base for MCMC to be applied to other fields as well.

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Product details

  • Hardcover: 512 pages
  • Publisher: Chapman and Hall/CRC; Softcover reprint of the original 1st ed. 1996 edition (1 Jan 1996)
  • Language: English
  • ISBN-10: 0412055511
  • ISBN-13: 978-0412055515
  • Product Dimensions: 24 x 15.6 x 3.1 cm
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Bestsellers Rank: 1,014,756 in Books (See Top 100 in Books)
  • See Complete Table of Contents

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Markov chain Monte Carlo (MCMC) methodology provides enormous scope for realistic statistical modelling. Read the first page
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11 of 12 people found the following review helpful
By A Customer
An excellent book to start learning about MCMC, the standard numerical strategy for computing the posterior probability distributions needed for Bayesian analysis. Contains a straightforward explanation of MCMC at the start, then has a number of chapters showing worked examples illustrating best practice across a range of application domains. Also has mathematical chapters covering relevant Markov chain theory and tips on speeding up convergence. ESSENTIAL FOR ALL USING MCMC!
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5.0 out of 5 stars Easy to understand, highly recommended 11 Jun 2013
Other books are very much focusing on how mathematically works. This book give you the right balance the theory is a mean of conceptually understand the meaning behind. To me, this is the right balance.

Personally I could not care less how mathematician come to the conclusion of a formula. it is more important that I can understand it from logical point of view. People can also explain better with this book.
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Most Helpful Customer Reviews on (beta) 4.3 out of 5 stars  3 reviews
31 of 34 people found the following review helpful
5.0 out of 5 stars MCMC methods presented for efficient and realistic application of Bayesian methods 8 Feb 2008
By Michael R. Chernick - Published on
Gilks, Richardson and Spiegelhalter edited this marvelous collection of papers on applications of Markov Chain Monte Carlo methods. There has been a big payoff for Bayesians as this method has been a breakthrough for dealing with flexible prior distributions. Most (but not all) of the articles deal with Bayesian applications. The editors themselves start out with an introductory chapter that covers the basic ideas and sets the stage for the articles to come. They provide many references including several of the articles in this volume.
The list of authors is quite impressive and many interesting examples are presented. The editors themselves contribute to other chapters. Spiegelhalter and Gilks co-authored a chapter on a Hepatitis B case study with Best and Inskip. Gilks has a chapter on full conditional distributions and co-authors a chapter on strategies for improving the MCMC algorithms. Richardson contributes a chapter on measurement error.

George and McCulloch deal with the use of Gibbs sampling to choose variables in a model based on a Bayesian approach. Raftery also has a chapter on Bayesian approaches in hypothesis testing and model selection. Green covers image analysis. There are many others (25 chapters in all). This is a great reference for anyone interested in MCMC methods.

The BUGS (Bayesian inference Using Gibbs Sampling)software was developed by Spiegelhalter, Thomas, Best and Gilks to implement Gibbs sampling in a variety of contexts. They illustrate its use along with the diagnostic software CODA in the application in Chapter 2. It is also mentioned in various other chapters in the book. There is currently a version called winBUGS which is designed for Windows operating systems.

Before jumping into the use of MCMC a user would be well advised to study this book.
15 of 41 people found the following review helpful
3.0 out of 5 stars Okay. 5 May 2005
By Falling Maple - Published on
First, I'll like to comment on the termiology. I'm PhD specializing in stochastic simulation in operations researcn and I've found the book is written in a language that's not quite standard (it might have something to do with his background in Statistics). Some people may argue that "names" are just "names" but it could cause confusion. And, in the chapter of stochastic approximation, the author failed to mention a couple of well-known existing methodology (somehow show a poor literature review in the field.) Strong emphasis has been given on importance sampling on that particular chapter, but author failed to mention in what context will importance sampling work. If you assume Bayesian approach and have prior on the parameters, then it works. But, if you're a frequentist, it's not necessarily working for your model.

Going back to the first chapter, I found the construction of MCMC is presented much more clearly in Sheldon Ross's Probability Model rather than this book.
11 of 43 people found the following review helpful
5.0 out of 5 stars Very Useful. 25 Oct 1997
By - Published on
We recommend this book to anyone who is interested in learning MCMC methods. Contains a excellent selection of practical examples. Christopher Gordon and Steve Hirschowitz
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