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Bayesian Theory and Applications$
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Bayesian Theory and Applications

Paul Damien, Petros Dellaportas, Nicholas G. Polson, and David A. Stephens

Print publication date: 2013

Print ISBN-13: 9780199695607

Published to Oxford Scholarship Online: May 2013

DOI: 10.1093/acprof:oso/9780199695607.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 19 January 2021

Advances in Markov chain Monte Carlo

Advances in Markov chain Monte Carlo

Chapter:
(p.104) 7 Advances in Markov chain Monte Carlo
Source:
Bayesian Theory and Applications
Author(s):

Griffin Jim E

Stephens David A

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199695607.003.0007

This chapter traces some of the key developments that further developed the underpinning theory and potential applications of Markov chain Monte Carlo (MCMC) since the mid 1990s. In particular, it reviews three main developments: reversible jump or transdimensional MCMC, population MCMC methods, and adaptive MCMC.

Keywords:   reversible jump MCMC, transdimensional MCMC, population MCMC methods, adaptive MCMC

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