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

Chapman and Hall/CRC, 1995. – 512 p. – ISBN: 0412055511, 9780412055515In 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 – W. R. Gilks, S. Richardson and D. J. Spiege/halter.
Hepatitis B: a case study in MCMC methods – D. J. Spiege/halter, N. G. Best, W. R. Gilks and H. Inskip.
Markov chain concepts related to sampling algorithms – G. O. Roberts.
Introduction to general state-space Markov chain theory – L. Tierney.
Full conditional distributions – W. R. Gilks.
Strategies for improving MCMC – W. R. Gilks and G. O. Roberts.
Implementing MCMC – A. E. Raftery and S. M. Lewis.
Inference and monitoring convergence – A. Gelman.
Model determination using sampling-based methods – A. E. Gelfand.
Hypothesis testing and model selection – A. E. Raftery.
Model checking and model improvement – A. Gelman and X-L. Meng.
Stochastic search variable selection – E. George and R. E. McCuloch.
Bayesian model comparison via jump diffusions – D. B. Philips and A. F. M. Smith.
Estimation and optimization of functions – C. J. Geyer.
Stochastic EM: method and application – J. Diebot and E. H. S. Ip.
Generalized linear mixed models – D. G. Clayton.
MCMC for nonlinear hierarchical models – J. E. Bennett, A. Racine-Poon and J. C. Wakefield.
Bayesian mapping of disease – A. Molie.
MCMC in image analysis – P. J. Green.
Measurement error – S. Richardson.
Gibbs sampling methods in genetics – D. C. Thomas and W. J. Gauderman.
Mixtures of distributions: inference and estimation – C. P. Robert.
An archaeological example: radiocarbon dating – C. Litton and C. Buck.