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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. 相似文献
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