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Markov Chain Monte Carlo in Practice: Interdisciplinary Statistics
from: Chapman & Hall/CRC
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Binding: Hardcover
Dewey Decimal Number: 519.233
EAN: 9780412055515
Edition: 1
ISBN: 0412055511
Label: Chapman & Hall/CRC
Manufacturer: Chapman & Hall/CRC
Number Of Items: 1
Number Of Pages: 512
Publication Date: December 01, 1995
Publisher: Chapman & Hall/CRC
Studio: Chapman & Hall/CRC
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Editorial Review:
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.
Customer Reviews
Average Rating: 
Rating:  - MCMC methods presented for efficient and realistic application of Bayesian methods
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 ... Read More
Rating:  - Okay.
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 ... Read More
Rating: - great collection of articles on applications
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 ... Read More
Rating: - Very Useful.
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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