Bayesian Mixture Models for Latent Class Analysis

Bayesian mixture models provide rich and flexible class tools which are particularly useful when there is unobserved heterogeneity in the data. When the number of subpopulations, called components, is assumed random, we allow the data to determine the complexity of the model. The latter property allows us to include a finite mixture model with a random number of components into the wider class of Bayesian nonparametric models. In this paper we consider multivariate discrete data, so that the class of mixtures is also referred to as latent class models. In particular, we let the number of latent classes to be random, and resort to Bayesian nonparametric techniques to devise a MCMC algorithm. The model is illustrated on an benchmark application dealing with role conflict