Markov Chain Monte Carlo in Practice

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W.R. Gilks, S. Richardson, David Spiegelhalter
CRC Press, Dec 1, 1995 - Mathematics - 512 pages
3 Reviews
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.
  

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确实是一本好书。

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Contents

a case study in MCMC methods
21
Markov chain concepts related to sampling algorithms
45
Introduction to general statespace Markov chain theory
59
Full conditional distributions
75
Strategies for improving MCMC
89
Implementing MCMC
115
Inference and monitoring convergence
131
Andrew Gelman Department of Statistics
142
George MSIS Department
214
Bayesian model comparison via jump diffusions
215
Estimation and optimization of functions
241
method and application
259
Generalized linear mixed models
275
Medical monitoring
321
Bayesian mapping of disease
359
Measurement error
401

Model determination using samplingbased methods
145
Hypothesis testing and model selection
163
Model checking and model improvement
189
Stochastic search variable selection
203
Gibbs sampling methods in genetics
419
inference and estimation
441
radiocarbon dating
465
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Markov Chain Monte Carlo in Practice
Markov Chain Monte Carlo in Practice. wr Gilks, Senior Scientist, Medical Research Council Biostatistics Unit, Cambridge, UK S. Richardson, French National ...
www.lce.hut.fi/ teaching/ S-114.202/ k98/ mcmc_prac.html

JSTOR: Markov Chain Monte Carlo in Practice.
Markov Chain Monte Carlo in Practice. wr GILKS, S. RICHARDSON, and dj SPIEGELHALTER (Eds.). London: Chapman and Hall, 1996. xvii + 486 pp. $54.95. ...
links.jstor.org/ sici?sici=0162-1459(199712)92%3A440%3C1645%3AMCMCIP%3E2.0.CO%3B2-E

are separated exponentially, the trajectories of the system are ...
Markov Chain Monte Carlo in Practice is a valu-. able addition to what is currently a rather sparse. offering of titles on the important topic of MCMC ...
doi.wiley.com/ 10.1002/ (SICI)1097-0258(19980615)17:11%3C1301::AID-SIM882%3E3.0.CO;2-9

Markov Chain Monte Carlo in Practice: A Roundtable Discussion
Markov Chain Monte Carlo in Practice:. A Roundtable Discussion. Moderator: Robert E. Kass. 1. Panelists: Bradley P. Carlin. , Andrew ...
www.stat.columbia.edu/ ~gelman/ research/ published/ kass5.ps

Markov Chain Monte Carlo In Practice
Gilks, wr, Richardson, S. and Spiegelhalter, dj Markov Chain Monte Carlo In Practice. Introducing Markov Chain Monte Carlo The Problem, Markov Chain Monte ...
www.statistics.com/ resources/ books/ printed/ Bayes/ Gilks.php3

Markov Chain Monte Carlo in Practice: A Roundtable Discussion ...
Markov chain Monte Carlo MCMC methods make possible the use of flexible Bayesian models that would otherwise be computationally infeasible
citeseer.ist.psu.edu/ 117324.html

Markov Chain Monte Carlo in Practice: A Roundtable Discussion
Gelman, A. (1996), "Inference and Monitoring Convergence," in Markov Chain Monte Carlo in Practice, eds. wr Gilks, S. Richardson, and dj Spiegelhalter, ...
www.questia.com/ PM.qst?a=o& se=gglsc& d=5001349516

References for pkbugs site
Gilks, wr (1996) Full conditional distributions, in wr Gilks, S. Richardson, and dj Spiegelhalter (eds), Markov Chain Monte Carlo in Practice, Chapman and ...
www.winbugs-development.org.uk/ pkbugs/ references.html

MCMC tutorial
wr Gilks et al (eds) Markov Chain Monte Carlo in Practice, Chapman & Hall, 1996. [3]. P. Bremaud, Markov Chains: Gibbs Fields, Monte Carlo Simulation and ...
civs.ucla.edu/ MCMC/ MCMC_tutorial.htm

Mixtures of distributions: inference and estimation, in Markov ...
0.750 Markov Chain Monte Carlo in Practice, ser. 0.750 Markov Chain Monte Carlo in Practice,(1995), Boca Raton. 1 2 3 4 5 6 7 8 9 10 . ...
www.ist-world.org/ ResultPublicationDetails.aspx?ResultPublicationId=0e31edb48bc4492d846fc382f350ae75

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