| Presentation | 2002/1/22 Bayesian Stochastic Complexity and Its convergence in Law Sumio Watanabe, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Recently, it is being proven by theory and experiments that Bayesian ensemble learning attains the more precise inference than the maximum likelihood and the maximum a posteriori probability methods. The following three problems are becoming more important. (1) The reason why Bayes attains the good inference . (2) The method how to realize the a posteriori distribution. (3) The method how to devise the more precise inference.In this paper, we try to explain the role of the stochastic complexity in these problems. |
| Keyword(in Japanese) | (See Japanese page) |
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| Committee | NC |
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| Conference Date | 2002/1/22(1days) |
| Place (in Japanese) | (See Japanese page) |
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| Topics (in Japanese) | (See Japanese page) |
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| 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) | Bayesian Stochastic Complexity and Its convergence in Law |
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| 1st Author's Name | Sumio Watanabe |
| 1st Author's Affiliation | PI Lab., Tokyo Institute of Technology() |
| Date | 2002/1/22 |
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| Volume (vol) | vol.101 |
| Number (no) | 616 |
| Page | pp.pp.- |
| #Pages | 8 |
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