TY  - BOOK
AU  - Gilks,W.R.
AU  - Richardson,S.
AU  - Spiegelhalter,D.J.
TI  - Markov chain Monte Carlo in practice
SN  - 0412055511
AV  - QA274.7 .M355 1998
U1  - 519.233 21
PY  - 1998///
CY  - Boca Raton, Fla.
PB  - Chapman & Hall
KW  - Markov processes
KW  - Monte Carlo method
KW  - Medical statistics
KW  - Biometry
N1  - Previously published: London : Chapman & Hall, 1996; Includes bibliographical references and index; Introducing Markov chain Monte Carlo / W.R. Gilks, S. Richardson and D.J. Spiegelhalter -- Hepatitis B : a case study in MCMC methods / D.J. Spiegelhalter ... [et al.] -- 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. McCulloch -- 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 modeling / B.P. Carlin -- Medical monitoring / C. Berzuini -- MCMC for nonlinear hierarchical models / J.E. Bennett, A. Racine-Poon and J.C. Wakefield -- Bayesian mapping of disease / A. Mollié -- 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
N2  - "General state-space Markov chain theory has evolved to make it both more accessible and more powerful. Markov Chain Monte Carlo in Practice introduces MCMC methods and their applications while also providing some theoretical background. Considering the broad audience, the editors emphasize practice rather than theory and keep the technical content to a minimum. They offer step-by-step instructions for using the methods presented and show the importance of MCMC in real applications with examples ranging from the simple to the more complex in fields such as archaeology, astronomy, biostatistics, genetics, epidemiology, and image analysis."--Publisher description
ER  -