Abstract Various objects can be summarily described by probability distributions: groups of raw data, paths of stochastic processes, neighborhoods of an image pixel and so on. Dealing with nonparametric distributions, we propose a method for classifying such objects by estimating a finite mixture of Dirichlet distributions when the observed distributions are assumed to be outcomes of a finite mixture of Dirichlet processes. We prove the consistency of such a classification by using the mutual s.
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