Variational Bayes and Truncation approximations for Enriched Dirichlet process mixtures

Abstract

A common impediment in conducting inference for Bayesian nonparametric models is either the need for complex MCMC algorithms and/or computational run-time for large datasets. We propose solutions here for Enriched Dirichlet process mixtures (EDPM). We derive a variational Bayes estimator based on a previously developed truncation approximation for EDPMs. The variational Bayes estimator can be used in two ways: 1) to develop a more efficient truncation approximation; 2) as good initial values for a blocked Gibbs sampler based on this more efficient truncation approximation or for a polya urn sampler. We derive the accuracy of this more efficient truncation approximation and demonstrate how this allows for simple implementation of a blocked Gibbs Sampler EDPMs in Nimble. We confirm the validity of the approximations by simulations and illustrate on a real data set.