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

Nizar Bouguila; Djemel Ziou; Ernest Monga

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

机译：吉布斯采样对有限β混合物的实际贝叶斯估计及其应用

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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.

机译：本文涉及有限Beta混合模型的贝叶斯分析。我们依靠混合模型的缺失数据结构，提出了一种通过吉布斯采样来评估后验分布和贝叶斯估计量的近似方法。实验结果涉及上下文和非上下文评估。非上下文评估基于合成直方图，而上下文评估则对模式识别数据集的类条件密度进行建模。 Beta混合物也可用于估计SAR图像直方图的参数。

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来源
《Statistics and computing》 |2006年第2期|p.215-225|共11页
作者
Nizar Bouguila; Djemel Ziou; Ernest Monga;
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作者单位

Universite de Sherbrooke Sherbrooke, Qc, Canada J1K 2R1;

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原文格式 PDF
正文语种 eng
中图分类统计学;
关键词
beta distribution; mixture modeling; maximum likelihood; bayesian analysis; gibbs sampling; metropolis-hastings; EM; SEM; SAR images;

机译：β分布;混合模型;最大似然;贝叶斯分析;吉布斯抽样;大都市骚扰;EM;SEM;SAR图像;

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

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