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Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 2/2009

01.06.2009 | Theoretical Advances

On Bayesian analysis of a finite generalized Dirichlet mixture via a Metropolis-within-Gibbs sampling

verfasst von: Nizar Bouguila, Djemel Ziou, Riad I. Hammoud

Erschienen in: Pattern Analysis and Applications | Ausgabe 2/2009

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Abstract

In this paper, we present a fully Bayesian approach for generalized Dirichlet mixtures estimation and selection. The estimation of the parameters is based on the Monte Carlo simulation technique of Gibbs sampling mixed with a Metropolis-Hastings step. Also, we obtain a posterior distribution which is conjugate to a generalized Dirichlet likelihood. For the selection of the number of clusters, we used the integrated likelihood. The performance of our Bayesian algorithm is tested and compared with the maximum likelihood approach by the classification of several synthetic and real data sets. The generalized Dirichlet mixture is also applied to the problems of IR eye modeling and introduced as a probabilistic kernel for Support Vector Machines.
Vorheriger Artikel Image change detection from difference image through deterministic simulated annealing
Nächster Artikel Human tracking and silhouette extraction for human–robot interaction systems

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Metadaten
Titel
On Bayesian analysis of a finite generalized Dirichlet mixture via a Metropolis-within-Gibbs sampling
verfasst von
Nizar Bouguila
Djemel Ziou
Riad I. Hammoud
Publikationsdatum
01.06.2009
Verlag
Springer-Verlag
Erschienen in
Pattern Analysis and Applications / Ausgabe 2/2009
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-008-0111-4

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