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Bayesian finite mixtures with an unknown number of components: the allocation sampler

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Publication details

JournalStatistics and computing
DateE-pub ahead of print - 7 Feb 2007
DatePublished (current) - Jun 2007
Issue number2
Number of pages16
Pages (from-to)147 - 162
Early online date7/02/07
Original languageEnglish


A new Markov chain Monte Carlo method for the Bayesian analysis of finite mixture distributions with an unknown number of components is presented. The sampler is characterized by a state space consisting only of the number of components and the latent allocation variables. Its main advantage is that it can be used, with minimal changes, for mixtures of components from any parametric family, under the assumption that the component parameters can be integrated out of the model analytically. Artificial and real data sets are used to illustrate the method and mixtures of univariate and of multivariate normals are explicitly considered. The problem of label switching, when parameter inference is of interest, is addressed in a post-processing stage.

    Research areas

  • Classification, Galaxy data, Iris data, Label switching, Markov chain Monte Carlo, Multivariate normal mixtures, Normal mixtures, Reversible jump

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