Practical Bayesian estimation of a finite beta mixture through gibbs sampling and its applications

Authors:
Nizar Bouguila;Djemel Ziou;Ernest Monga
Affiliations:
Département d'Informatique, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, Canada J1K 2R1;Département d'Informatique, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, Canada J1K 2R1;Département d'Informatique, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, Canada J1K 2R1
Venue:
Statistics and Computing
Year:
2006

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Abstract

This paper deals with a Bayesian analysis of a finite Beta mixture model. We present approximation method to evaluate the posterior distribution and Bayes estimators by Gibbs sampling, relying on the missing data structure of the mixture model. Experimental results concern contextual and non-contextual evaluations. The non-contextual evaluation is based on synthetic histograms, while the contextual one model the class-conditional densities of pattern-recognition data sets. The Beta mixture is also applied to estimate the parameters of SAR images histograms.