Improved Bayesian information criterion for mixture model selection

Published in Pattern Recognition Letters, 2016

Recommended citation: Mehrjou, Arash, Reshad Hosseini, and Babak Nadjar Araabi. "Improved Bayesian information criterion for mixture model selection." Pattern Recognition Letters 60 (2016): 22-27

In this paper, we propose a mixture model selection criterion obtained from the Laplace approximation of marginal likelihood. Our approximation to the marginal likelihood is more accurate than Bayesian information criterion (BIC), especially for small sample size. We show experimentally that our criterion works as good as other well-known criteria like BIC and minimum message length (MML) for large sample size and significantly outperforms them when fewer data points are available.

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