| Presentation | 2004/10/12 Stochastic Complexity and Singularities in Hierarchical Bayesian Networks Keisuke YAMAZAKI, Sumio WATANABE, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Bayesian networks are now used in enormous fields, for example, system diagnosis, data mining, clusterings etc. In spite of wide range of their applications, the statistical properties have not yet been clarified because the models are nonidentifiable and non-regular. In recent years, however, we have developed a method to analyze non-regular models by using algebraic geometry and applied it to complete bipartite graph-type Bayesian networks. The models consist of two layers, one is observable and the other hidden. In this paper, extending the method to general hierarchical Bayesian networks, we clarify upper bounds of the asymptotic form of the stochastic complexities. We obtain a unified view of singularities in some models such as Bayesian networks, mixture models and hidden Markov models. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Bayesian network / Stochastic complexity / Algebraic geometry |
| Paper # | NC2004-73 |
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| Committee | NC |
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| Conference Date | 2004/10/12(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 | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Stochastic Complexity and Singularities in Hierarchical Bayesian Networks |
| Sub Title (in English) | |
| Keyword(1) | Bayesian network |
| Keyword(2) | Stochastic complexity |
| Keyword(3) | Algebraic geometry |
| 1st Author's Name | Keisuke YAMAZAKI |
| 1st Author's Affiliation | Precision and Intelligence Laboratory, Tokyo Institute of Technology() |
| 2nd Author's Name | Sumio WATANABE |
| 2nd Author's Affiliation | Precision and Intelligence Laboratory, Tokyo Institute of Technology |
| Date | 2004/10/12 |
| Paper # | NC2004-73 |
| Volume (vol) | vol.104 |
| Number (no) | 349 |
| Page | pp.pp.- |
| #Pages | 6 |
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