Convergence properties of a general algorithm for calculating variational Bayesian estimates for a normal mixture model

Abstract

In this paper we propose a generalised iterative algorithm for calculating variational Bayesian estimates for a normal mixture model and investigate its convergence properties. It is shown theoretically that the variational Bayesian estimator converges locally to the maximum likelihood estimator at the rate of $O(1/{n})$ in the large sample limit.