Design of exchange Monte Carlo method for Bayesian learning in normal mixture models

Authors:
Kenji Nagata;Sumio Watanabe
Affiliations:
P. & I Lab., Tokyo Institute of Technology, Yokohama, Japan;P. & I Lab., Tokyo Institute of Technology, Japan
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

Citing 4
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Abstract

The exchange Monte Carlo (EMC) method was proposed as an improved algorithm of Markov chain Monte Carlo method, and its effectiveness has been shown in spin-glass simulation, Bayesian learning and many other applications. In this paper, we propose a new algorithm of EMC method with Gibbs sampler by using the hidden variable representing the component from which the datum is generated, and show its effectiveness by the simulation of Bayesian learning of normal mixture models.