A Simple Solution to Bayesian Mixture Labeling

Abstract

The label-switching problem is one of the fundamental problems in Bayesian mixture analysis. Using all the Markov chain Monte Carlo samples as the initials for the expectation-maximization (EM) algorithm, we propose to label the samples based on the modes they converge to. Our method is based on the assumption that the samples converged to the same mode have the same labels. If a relative noninformative prior is used or the sample size is large, the posterior will be close to the likelihood and then the posterior modes can be located approximately by the EM algorithm for mixture likelihood, without assuming the availability of the closed form of the posterior. In order to speed up the computation of this labeling method, we also propose to first cluster the samples by K-means with a large number of clusters K. Then, by assuming that the samples within each cluster have the same labels, we only need to find one converged mode for each cluster. Using a Monte Carlo simulation study and a real dataset, we demonstrate the success of our new method in dealing with the label-switching problem.