| Presentation | 2002/1/22 New Development of the EM algorithm : Variational Bayes Naonori UEDA, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | This report provides a tutorial on Variational Bayes (VB), a practical framework for Bayesian computations. First, I review the EM method which is a general procedure for obtaining maximum likelihood estimates and also explain the generalized EM (GEM) method. Then, the basic principle of the VB method can be interpreted as Bayesian extension of variational approximation used in the GEM method. Namely, I explain how the EM method has develped into the VB method. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Bayes learning / Variational Bayes / EM method / Variational approximation |
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| Conference Information | |
| Committee | NC |
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| Conference Date | 2002/1/22(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| 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) | New Development of the EM algorithm : Variational Bayes |
| Sub Title (in English) | |
| Keyword(1) | Bayes learning |
| Keyword(2) | Variational Bayes |
| Keyword(3) | EM method |
| Keyword(4) | Variational approximation |
| 1st Author's Name | Naonori UEDA |
| 1st Author's Affiliation | NTT Communication Science Laboratorie() |
| Date | 2002/1/22 |
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| Volume (vol) | vol.101 |
| Number (no) | 616 |
| Page | pp.pp.- |
| #Pages | 8 |
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