PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Convergence properties of a general algorithm for calculating variational Bayesian estimates for a normal mixture model
Bo Wang and Mike Titterington
Bayesian Analysis 2004.

Abstract

In this paper we propose a generalised iterative algorithm for calculating variational Bayesian estimates for a normal mixture model and we investigate its convergence properties. It is shown theoretically that the variational Bayes estimator converges locally to the maximum likelihood estimator at the rate of O(1/n) in the large sample limit. We also demonstrate by numerical experiments that the generalised algorithm can be accelerated by a suitable choice of step size.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:431
Deposited By:Mike Titterington
Deposited On:22 December 2004