An online Bayesian mixture labelling method by minimizing deviance of classification probabilities to reference labels

dc.citation.doidoi:10.1080/00949655.2012.707201en_US
dc.citation.epage323en_US
dc.citation.issue2en_US
dc.citation.jtitleJournal of Statistical Computation and Simulationen_US
dc.citation.spage310en_US
dc.citation.volume84en_US
dc.contributor.authorYao, Weixin
dc.contributor.authorLi, Longhai
dc.contributor.authoreidwxyaoen_US
dc.date.accessioned2014-03-10T20:05:16Z
dc.date.available2014-03-10T20:05:16Z
dc.date.issued2014-03-10
dc.date.published2014en_US
dc.description.abstractSolving label switching is crucial for interpreting the results of fitting Bayesian mixture models. The label switching originates from the invariance of posterior distribution to permutation of component labels. As a result, the component labels in Markov chain simulation may switch to another equivalent permutation, and the marginal posterior distribution associated with all labels may be similar and useless for inferring quantities relating to each individual component. In this article, we propose a new simple labelling method by minimizing the deviance of the class probabilities to a fixed reference labels. The reference labels can be chosen before running Markov chain Monte Carlo (MCMC) using optimization methods, such as expectation-maximization algorithms, and therefore the new labelling method can be implemented by an online algorithm, which can reduce the storage requirements and save much computation time. Using the Acid data set and Galaxy data set, we demonstrate the success of the proposed labelling method for removing the labelling switching in the raw MCMC samples.en_US
dc.identifier.urihttp://hdl.handle.net/2097/17211
dc.language.isoen_USen_US
dc.relation.urihttp://www.tandfonline.com/doi/full/10.1080/00949655.2012.707201#.Uxo0vj9dXL8en_US
dc.rightsThis is an electronic version of an article published in Journal of Statistical Computation and Simulation, 84(2), 310-323. Journal of Statistical Computation and Simulation is available online at: http://www.tandfonline.com/doi/full/10.1080/00949655.2012.707201#.Uxo0vj9dXL8en_US
dc.subjectBayesian mixturesen_US
dc.subjectLabel switchingen_US
dc.subjectMarkov chain Monte Carloen_US
dc.subjectMixture modelsen_US
dc.subjectRelabelingen_US
dc.titleAn online Bayesian mixture labelling method by minimizing deviance of classification probabilities to reference labelsen_US
dc.typeArticle (author version)en_US

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