Markov chain Monte Carlo, recent developments
Mengersen, Kerrie L. (2005) Markov chain Monte Carlo, recent developments. In Armitage, Peter & Colton, Theodore (Eds.) Encyclopedia of Biostatistics. John Wiley & Sons, Chichester, West Sussex.
There has recently been an explosion of interest in Markov chain Monte Carlo (MCMC) for biostatistical modeling and analysis, particularly, but not exclusively in a Bayesian framework. The aim of this update is to describe some of the new developments in MCMC methodology and their application in biostatistics. MCMC algorithms, including variations on Metropolis and Gibbs, reversible jump MCMC, slice sampling perfect simulation, delayed rejection, and sequential Monte Carlo are briefly discussed. The important issue of MCMC convergence is also addressed. Applications of MCMC in areas of interest in biostatistics are illustrated through references to CART, latent variable models, hidden Markov models, time series models, factorial experiments, correlated data, meta-analysis, clinical trials, and bioinformatics. The expanding array of software available for MCMC is acknowledged, followed by references to recent biostatistics-related books on the topic.
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|Item Type:||Book Chapter|
|Additional Information:||2nd Edition|
|Keywords:||Markov chain Monte Carlo, MCMC|
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright 2005 John Wiley & Sons|
|Copyright Statement:||For more information about this book please refer to the publisher's website (see link) or contact the author. Author contact details : email@example.com|
|Deposited On:||12 Apr 2005|
|Last Modified:||03 Mar 2011 05:33|
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