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

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dc.contributor.author Yao, Weixin
dc.contributor.author Li, Longhai
dc.date.accessioned 2014-03-10T20:05:16Z
dc.date.available 2014-03-10T20:05:16Z
dc.date.issued 2014-03-10
dc.identifier.uri http://hdl.handle.net/2097/17211
dc.description.abstract Solving 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.language.iso en_US en_US
dc.relation.uri http://www.tandfonline.com/doi/full/10.1080/00949655.2012.707201#.Uxo0vj9dXL8 en_US
dc.rights This 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#.Uxo0vj9dXL8 en_US
dc.subject Bayesian mixtures en_US
dc.subject Label switching en_US
dc.subject Markov chain Monte Carlo en_US
dc.subject Mixture models en_US
dc.subject Relabeling en_US
dc.title An online Bayesian mixture labelling method by minimizing deviance of classification probabilities to reference labels en_US
dc.type Article (author version) en_US
dc.date.published 2014 en_US
dc.citation.doi doi:10.1080/00949655.2012.707201 en_US
dc.citation.epage 323 en_US
dc.citation.issue 2 en_US
dc.citation.jtitle Journal of Statistical Computation and Simulation en_US
dc.citation.spage 310 en_US
dc.citation.volume 84 en_US
dc.contributor.authoreid wxyao en_US


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