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Giks W.R., Richardson S., Spiegelhalter D.J. (Eds.) Markov Chain Monte Carlo in Practice

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Giks W.R., Richardson S., Spiegelhalter D.J. (Eds.) Markov Chain Monte Carlo in Practice
New York: Chapman and Hall/CRC, 1995. — 512 p.
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.
Introducing Markov chain Monte Carlo
Hepatitis B: a case study in MCMC methods
Markov chain concepts related to sampling algorithms
Introduction to general state-space Markov chain theory
Full conditional distributions
Strategies for improving MCMC
Implementing MCMC
Inference and monitoring convergence
Model determination using sampling-based methods
Hypothesis testing and model selection
Model checking and model improvement
Stochastic search variable selection
Bayesian model comparison via jump diffusions
Estimation and optimization of functions
Stochastic EM: method and application
Generalized linear mixed models
MCMC for nonlinear hierarchical models
Bayesian mapping of disease .
MCMC in image analysis
Measurement error
Gibbs sampling methods in genetics
Mixtures of distributions: inference and estimation
An archaeological example: radiocarbon dating
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