| Presentation | 2002/1/22 Graphical Models and Variational Principles for Mean Field Approximations Yoshiyuki KABASHIMA, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Mean field approximations are practical methods to make large scale statistical models computationally tractable. In this paper, we show that a variation of mean field approximations, termed the Bethe approximation or the loopy belief propagation, can be derived by extending a variational principle which is validated for a class of statistical models characterized by junction trees to general graphical models. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Mean field approximations / Bethe approximation / belief propagation / junction trees / graphical models |
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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) | Graphical Models and Variational Principles for Mean Field Approximations |
| Sub Title (in English) | |
| Keyword(1) | Mean field approximations |
| Keyword(2) | Bethe approximation |
| Keyword(3) | belief propagation |
| Keyword(4) | junction trees |
| Keyword(5) | graphical models |
| 1st Author's Name | Yoshiyuki KABASHIMA |
| 1st Author's Affiliation | Department of Computational Intelligence and Systems Science, 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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