BAYESIAN APPROACHES TO GAUSSIAN MIXTURE MODELING

A Bayesian-based methodology is presented which automatically penalize s 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 pe rformance of the Bayesian method is compared to other methods of optim al model selection and found to give good results. The methods are tes ted on synthetic and real data sets.