A symmetrized, ordering-constrained THAMES estimator accurately computes marginal likelihoods for univariate and multivariate Gaussian mixtures, demonstrated up to 15 components.
Bayesian finite mixtures: a note on prior specification and posterior computation
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abstract
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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Easily Computed Marginal Likelihoods for Multivariate Mixture Models Using the THAMES Estimator
A symmetrized, ordering-constrained THAMES estimator accurately computes marginal likelihoods for univariate and multivariate Gaussian mixtures, demonstrated up to 15 components.