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Bayesian finite mixtures: a note on prior specification and posterior computation

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arxiv 0711.0458 v1 pith:CYOLL22X submitted 2007-11-03 stat.ME stat.CO

classification stat.MEstat.CO
keywords priorcomponentscomputationdistributionfinitemixtureposteriorspecification
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A new method for the computation of the posterior distribution of the number k of components in a finite mixture is presented. Two aspects of prior specification are also studied: an argument is made for the use of a Poisson(1) distribution as the prior for k; and methods are given for the selection of hyperparameter values in the mixture of normals model, with natural conjugate priors on the components parameters.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Simulation-consistent Estimation of the Marginal Likelihood for Block Models

    stat.ME 2026-07 conditional novelty 6.0 of 10

    A truncated harmonic mean estimator computes stochastic-block-model marginal likelihoods from MCMC samples, achieving simulation-consistency at fixed data size and handling label switching efficiently.

  2. Easily Computed Marginal Likelihoods for Multivariate Mixture Models Using the THAMES Estimator

    stat.ME 2025-04 conditional novelty 6.0 of 10

    A symmetrized, ordering-constrained THAMES estimator accurately computes marginal likelihoods for univariate and multivariate Gaussian mixtures, demonstrated up to 15 components.

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