Bayesian approaches to Gaussian mixture modeling

Roberts, S., Husmeier, D. , Rezek, L. and Penny, W. (1998) Bayesian approaches to Gaussian mixture modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), pp. 1133-1142. (doi:10.1109/34.730550)

Full text not currently available from Enlighten.

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.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Husmeier, Professor Dirk
Authors: Roberts, S., Husmeier, D., Rezek, L., and Penny, W.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher:Institute of Electrical and Electronics Engineers
ISSN:0162-8828

University Staff: Request a correction | Enlighten Editors: Update this record