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Design of Exchange Monte Carlo Method for Bayesian Learning in Normal Mixture Models

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Advances in Neuro-Information Processing (ICONIP 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5506))

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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.

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References

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Author information

Authors and Affiliations

  1. P. & I Lab., Tokyo Institute of Technology, MailBox: R2-5, 4259, Nagatsuta-chou, Midori-ku, Yokohama, 226-8503, Japan

    Kenji Nagata

  2. P. & I Lab., Tokyo Institute of Technology, Japan

    Sumio Watanabe

Authors
  1. Kenji Nagata
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  2. Sumio Watanabe
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Editor information

Editors and Affiliations

  1. Kyushu Institute of Technology, Network Design and Research Center, 680-4 Fukuoka, 820-8502, Kawazu, Iizuka, Japan

    Mario Köppen

  2. Knowledge Engineering and Discovery Research Institute (KEDRI), School of Computing and Mathematical Sciences, Auckland University of Technology, 350 Queen Street, 10110, Auckland, New Zealand

    Nikola Kasabov

  3. Department of Electrical and Computer Engineering, Robotics Laboratory, Auckland University of Technology, 38 Princes Street, 1142, Auckland, New Zealand

    George Coghill

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© 2009 Springer-Verlag Berlin Heidelberg

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Nagata, K., Watanabe, S. (2009). Design of Exchange Monte Carlo Method for Bayesian Learning in Normal Mixture Models. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_85

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  • DOI: https://doi.org/10.1007/978-3-642-02490-0_85

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02489-4

  • Online ISBN: 978-3-642-02490-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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