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

被引：91

作者：

Bouguila, N ^{[1
]}

Ziou, D ^{[1
]}

Monga, E ^{[1
]}

机构：

[1] Univ Sherbrooke, Fac Sci, Dept Informat, Sherbrooke, PQ J1K 2R1, Canada

来源：

STATISTICS AND COMPUTING | 2006年 / 16卷 / 02期

基金：

加拿大自然科学与工程研究理事会;

关键词：

beta distribution; mixture modeling; maximum likelihood; Bayesian analysis; Gibbs sampling; metropolis-Hastings; EM; SEM; SAR images;

D O I：

10.1007/s11222-006-8451-7

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：215 / 225

页数：11

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