Variational approximations in Bayesian model selection for finite mixture distributions

McGrory, Clare A. & Titterington, D. M. (2007) Variational approximations in Bayesian model selection for finite mixture distributions. Computational Statistics & Data Analysis, 51(11), pp. 5352-5367.

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Variational methods, which have become popular in the neural computing/machine learning literature, are applied to the Bayesian analysis of mixtures of Gaussian distributions. It is also shown how the deviance information criterion, (DIC), can be extended to these types of model by exploiting the use of variational approximations. The use of variational methods for model selection and the calculation of a DIC are illustrated with real and simulated data. The variational approach allows the simultaneous estimation of the component parameters and the model complexity. It is found that initial selection of a large number of components results in superfluous components being eliminated as the method converges to a solution. This corresponds to an automatic choice of model complexity. The appropriateness of this is reflected in the DIC values.

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68 citations in Scopus
54 citations in Web of Science®
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ID Code: 14822
Item Type: Journal Article
Refereed: Yes
Keywords: Bayesian analysis, Deviance information criterion (DIC), Mixtures, Variational approximations
DOI: 10.1016/j.csda.2006.07.020
ISSN: 0167-9473
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Statistical Theory (010405)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2007 Elsevier
Copyright Statement: Reproduced in accordance with the copyright policy of the publisher.
Deposited On: 12 Sep 2008 00:00
Last Modified: 29 Feb 2012 13:39

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