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 Index
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