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Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling

Abstract:

In the past ten years there has been a dramatic increase of interest in the Bayesian analysis of finite mixture models. This is primarily because of the emergence of Markov chain Monte Carlo (MCMC) methods. While MCMC provides a convenient way to draw inference from complicated statistical models, there are many, perhaps underappreciated, problems associated with the MCMC analysis of mixtures. The problems are mainly caused by the nonidentifiability of the components under symmetric priors, w...

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Publication status:
Published

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Publisher copy:
10.1214/08834230500000016

Authors


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Institution:
University of Oxford
Division:
MSD
Department:
NDM
Role:
Author
Journal:
STATISTICAL SCIENCE More from this journal
Volume:
20
Issue:
1
Pages:
50-67
Publication date:
2005-02-01
DOI:
ISSN:
0883-4237
Language:
English
Keywords:
Pubs id:
pubs:97552
UUID:
uuid:22f4c5a7-b919-41e6-bb55-271c241d388d
Local pid:
pubs:97552
Source identifiers:
97552
Deposit date:
2012-12-19

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