Bayesian mixture labelling by highest posterior density

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Show simple item record Yao, Weixin Lindsay, Bruce G. 2012-05-29T14:11:01Z 2012-05-29T14:11:01Z 2012-05-29
dc.description.abstract A fundamental problem for Bayesian mixture model analysis is label switching, which occurs due to the non-identifiability of the mixture components under symmetric priors. We propose two labelling methods to solve this problem. The first method, denoted by PM(ALG), is based on the posterior modes and an ascending algorithm generically denoted ALG. We use each Markov chain Monte Carlo (MCMC) sample as the starting point in an ascending algorithm, and label the sample based on the mode of the posterior to which it converges. Our natural assumption here is that the samples converged to the same mode should have the same labels. The PM(ALG) labelling method has some computational advantages over other popular labelling methods. Additionally, it automatically matches the “ideal” labels in the highest posterior density credible regions. The second method does labelling by maximizing the normal likelihood of the labelled Gibbs samples. Using a Monte Carlo simulation study and a real data set, we demonstrate the success of our new methods in dealing with the label switching problem. en_US
dc.relation.uri en_US
dc.rights This is an electronic version of an article published in Journal of the American Statistical Association, 104(486), 758-767. Journal of the American Statistical Association is available online at: en_US
dc.subject Label switching en_US
dc.subject Bayesian approach en_US
dc.subject Morkov chain Monte Carlo en_US
dc.subject Mixture mode en_US
dc.subject Posterior modes en_US
dc.title Bayesian mixture labelling by highest posterior density en_US
dc.type Article (author version) en_US 2009 en_US
dc.citation.doi doi: 10.1198/jasa.2009.0237 en_US
dc.citation.epage 767 en_US
dc.citation.issue 486 en_US
dc.citation.jtitle Journal of the American Statistical Association en_US
dc.citation.spage 758 en_US
dc.citation.volume 104 en_US
dc.contributor.authoreid wxyao en_US

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