Markov Chain Monte Carlo in Practice

Front Cover
W.R. Gilks, S. Richardson, David Spiegelhalter
CRC Press, Dec 1, 1995 - Mathematics - 512 pages
3 Reviews
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.
  

What people are saying - Write a review

User Review - Flag as inappropriate

确实是一本好书。

Contents

a case study in MCMC methods
21
Markov chain concepts related to sampling algorithms
45
Introduction to general statespace Markov chain theory
59
Full conditional distributions
75
Strategies for improving MCMC
89
Implementing MCMC
115
Inference and monitoring convergence
131
Andrew Gelman Department of Statistics
142
George MSIS Department
214
Bayesian model comparison via jump diffusions
215
Estimation and optimization of functions
241
method and application
259
Generalized linear mixed models
275
Medical monitoring
321
Bayesian mapping of disease
359
Measurement error
401

Model determination using samplingbased methods
145
Hypothesis testing and model selection
163
Model checking and model improvement
189
Stochastic search variable selection
203
Gibbs sampling methods in genetics
419
inference and estimation
441
radiocarbon dating
465
Copyright

Common terms and phrases

References to this book

All Book Search results »

Bibliographic information