| Presentation | 2005/3/23 A Proposal and Effectiveness of the Optimal Approximation for Bayesian Posterior Distribution Kenji NAGATA, Sumio WATANABE, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | A lot of learning machines such as neural networks, normal mixtures, Bayesian networks, and hidden Markov models are singular statistical models. When the Bayesian posterior distribution is approximated by Markov Chain Monte Carlo method, it requires huge costs. In this paper, we propose a new method to approximate the Bayesian posterior distribution of the singular statistical models by a comparatively simple distribution. And the properties of the proposed method are shown by experimetal results. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Singular Learning Machines / Bayesian Posterior distribution / Markov Chain Monte Carlo (MCMC) Method / Stochastic Complexity |
| Paper # | NC2004-226 |
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| Committee | NC |
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| Conference Date | 2005/3/23(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Neurocomputing (NC) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Proposal and Effectiveness of the Optimal Approximation for Bayesian Posterior Distribution |
| Sub Title (in English) | |
| Keyword(1) | Singular Learning Machines |
| Keyword(2) | Bayesian Posterior distribution |
| Keyword(3) | Markov Chain Monte Carlo (MCMC) Method |
| Keyword(4) | Stochastic Complexity |
| 1st Author's Name | Kenji NAGATA |
| 1st Author's Affiliation | Department of Computational Intelligence & Systems Science Tokyo Institute of Technology() |
| 2nd Author's Name | Sumio WATANABE |
| 2nd Author's Affiliation | P&I Lab., Tokyo Institute of Technology |
| Date | 2005/3/23 |
| Paper # | NC2004-226 |
| Volume (vol) | vol.104 |
| Number (no) | 760 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |