Mixture model fitting using conditional models and modal Gibbs sampling

In this paper, we present a novel approach to fitting mixture models based on estimating first the posterior distribution of the auxiliary variables that assign each observation to a group in the mixture. The posterior distributions of the remainder of the parameters in the mixture is obtained by averaging over their conditional posterior marginals on the auxiliary variables using Bayesian model averaging... (read more)

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