Bayesian mixture labeling and clustering

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Show simple item record Yao, Weixin 2012-07-11T22:39:51Z 2012-07-11T22:39:51Z 2012-07-11
dc.description.abstract Label switching is one of the fundamental issues for Bayesian mixture modeling. It occurs due to the nonidentifiability of the components under symmetric priors. Without solving the label switching, the ergodic averages of component specific quantities will be identical and thus useless for inference relating to individual components, such as the posterior means, predictive component densities, and marginal classification probabilities. In this article, we establish the equivalence between the labeling and clustering and propose two simple clustering criteria to solve the label switching. The first method can be considered as an extension of K-means clustering. The second method is to find the labels by minimizing the volume of labeled samples and this method is invariant to the scale transformation of the parameters. Using a simulation example and two real data sets application, 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 Communications in Statistics—Theory and Methods, 41(3), 403-421. Communications in Statistics—Theory and Methods is available online at: en_US
dc.subject Bayesian mixtures en_US
dc.subject Clustering en_US
dc.subject K-means en_US
dc.subject Label switching en_US
dc.subject Markov chain Monte Carlo en_US
dc.title Bayesian mixture labeling and clustering en_US
dc.type Article (author version) en_US 2012 en_US
dc.citation.doi doi:10.1080/03610926.2010.526741 en_US
dc.citation.epage 421 en_US
dc.citation.issue 3 en_US
dc.citation.jtitle Communications in Statistics—Theory and Methods en_US
dc.citation.spage 403 en_US
dc.citation.volume 41 en_US
dc.contributor.authoreid wxyao en_US

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