Prequential Bayes mixture approach for Gaussian mixture order selection

K. Gilbert, I. Bilik, J. Buck, K. Payton

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents a modified prequential Bayes (MPB) method for model order estimation of Gaussian mixture models (GMM). The proposed MPB order estimators recursively update the weighting for each order in a class of model orders from the mixture of a time-invariant prior and the likelihood of the observed data for each model. This paper investigates both a maximum a posteriori (MAP) switching version and an affine version of the MPB order estimator. Simulations demonstrate that the proposed MPB estimators are more accurate for small sample sizes than the minimum description length (MDL) criterion and the Akaike information criterion (AIC).

Original languageEnglish
Title of host publication2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010
Pages173-176
Number of pages4
DOIs
StatePublished - 20 Dec 2010
Externally publishedYes
Event2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010 - Jerusalem, Israel
Duration: 4 Oct 20107 Oct 2010

Publication series

Name2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010

Conference

Conference2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010
Country/TerritoryIsrael
CityJerusalem
Period4/10/107/10/10

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