Journal
STATISTICS AND COMPUTING
Volume 14, Issue 4, Pages 343-355Publisher
KLUWER ACADEMIC PUBL
DOI: 10.1023/B:STCO.0000039484.36470.41
Keywords
Gaussian mixture model; Markov chain Monte Carlo; Bayesian inference; model selection; reversible jump methodology; split and combine moves
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This paper is a contribution to the methodology of fully Bayesian inference in a multivariate Gaussian mixture model using the reversible jump Markov chain Monte Carlo algorithm. To follow the constraints of preserving the first two moments before and after the split or combine moves, we concentrate on a simplified multivariate Gaussian mixture model, in which the covariance matrices of all components share a common eigenvector matrix. We then propose an approach to the construction of the reversible jump Markov chain Monte Carlo algorithm for this model. Experimental results on several data sets demonstrate the efficacy of our algorithm.
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