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arXiv · 1106.4432

An approximate Bayesian marginal likelihood approach for estimating finite mixtures

Ryan Martin
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Abstract

Estimation of finite mixture models when the mixing distribution support is unknown is an important problem. This paper gives a new approach based on a marginal likelihood for the unknown support. Motivated by a Bayesian Dirichlet prior model, a computationally efficient stochastic approximation version of the marginal likelihood is proposed and large-sample theory is presented. By restricting the support to a finite grid, a simulated annealing method is employed to maximize the marginal likelihood and estimate the support. Real and simulated data examples show that this novel stochastic approximation--simulated annealing procedure compares favorably to existing methods.

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Ryan Martin. 2012-02-13. An approximate Bayesian marginal likelihood approach for estimating finite mixtures. https://doi.org/10.1080/03610918.2012.667476

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