Bayesian Inference for Mixture: The Label Switching Problem
Gilles Celeux · COMPSTAT · 1998
A K -component mixture distribution is invariant to permutations of the labels of the components. As a consequence, in a Bayesian framework, the posterior distribution of the mixture parameters has theoretically K ! modes. This fact involves possible difficulties when interpreting this posterior distribution. In this paper, we discuss the problem of labelling and we propose a simple and general clustering-like tool to deal with this problem.