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Table of contents:
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.
Contents:
Introducing Markov Chain Monte Carlo W.R
Gilks, S
Richardson, and D.J
Spielgelhalter Hepatitis B: A Case Study in MCMC Methods D.J
Spielgelhalter, 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.I
George and R.E
McColluch Bayesian Model Comparison via Jump Diffusions D.B
Phillips and A.F.M
Smith Estimation and Optimization of Functions C.J
Geyer Stochastic EM: Method and Application J
Diebolt and E.H.S
Ip Generalized Linear Mixed Models D.G
Clayton Hierarchical Longitudinal Modelling B.P
Carlin Medical Monitoring C
Berzuini MCMC for Nonlinear Hierarchical Models J.E
Bennet, A
Racine-Poon, and J.C
Wakefield Bayesian Mapping of Disease A
MolliT 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 Index
Brief Description:
This work introduces Markov chain Monte Carlo methodology at a level suitable for applied statisticians. It explains the methodology and its theoretical background, summarizes application areas, and presents illustrative applications in many areas including archaeology and astronomy.
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