Bayesian Approaches to Gaussian Mixture Modeling

Authors:
Stephen J. Roberts;Dirk Husmeier;William Penny;lead Rezek
Affiliations:
Imperial College of Science, London, UK;Imperial College of Science, London, UK;Imperial College of Science, London, UK;Imperial College of Science, London, UK
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1998

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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.