Markov Chain Monte Carlo in Practice: Interdisciplinary Statistics | 
 enlarge | Creators: W.r. Gilks, S. Richardson, David Spiegelhalter
Publisher: Chapman & Hall/CRC
Category: Book
List Price: $99.95
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Rating:  4 reviews
Sales Rank: 53118
Media: Hardcover
Edition: 1
Pages: 512
Number Of Items: 1
Shipping Weight (lbs): 1.8
Dimensions (in): 9.3 x 6.1 x 1.3
ISBN: 0412055511
Dewey Decimal Number: 519.233
EAN: 9780412055515
Publication Date: December 1, 1995
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Product Description
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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| Customer Reviews:
 great collection of articles on applications December 19, 2000
Michael R. Chernick (Malvern, PA)
39 out of 40 found this review helpful
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.
MCMC methods presented for efficient and realistic application of Bayesian methods February 8, 2008
Michael R. Chernick (Holland PA)
23 out of 24 found this review helpful
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.
Very Useful. October 25, 1997
chris_gordon1@rocketmail.com (UK)
10 out of 40 found this review helpful
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
 Okay. May 5, 2005
Falling Maple (Boston, MA)
9 out of 27 found this review helpful
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.
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