Allocation variable-based probabilistic algorithm to deal with label switching problem in Bayesian mixture models

Jia Chiun Pan, Chih Min Liu, Hai Gwo Hwu, Guan-Hua Huang

Research output: Contribution to journalArticlepeer-review

Abstract

The label switching problem occurs as a result of the nonidentifiability of posterior distribution over various permutations of component labels when using Bayesian approach to estimate parameters in mixture models. In the cases where the number of components is fixed and known, we propose a relabelling algorithm, an allocation variable-based (denoted by AVP) probabilistic relabelling approach, to deal with label switching problem. We establish a model for the posterior distribution of allocation variables with label switching phenomenon. The AVP algorithm stochastically relabel the posterior samples according to the posterior probabilities of the established model. Some existing deterministic and other probabilistic algorithms are compared with AVP algorithm in simulation studies, and the success of the proposed approach is demonstrated in simulation studies and a real dataset.

Original languageEnglish
Article numbere0138899
JournalPLoS ONE
Volume10
Issue number10
DOIs
StatePublished - 12 Oct 2015

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