Bayesian approaches to Gaussian mixture modeling

A Bayesian-based methodology is presented which automatically penalizes overcomplex models being fitted to unknown data and is able to select an "optimal" number of components in the model and so partition data sets.

Abstract

A Bayesian-based methodology is presented which automatically penalizes overcomplex models being fitted to unknown data. We show that, with a Gaussian mixture model, the approach is able to select an "optimal" number of components in the model and so partition data sets. The performance of the Bayesian method is compared to other methods of optimal model selection and found to give good results. The methods are tested on synthetic and real data sets.