| Presentation | 2007-07-25 On the Computation Approach of Learning Coefficients by Weighted Resolution of Singularities Takeshi MATSUDA, Sumio WATANABE, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Learning models which do not satisfy a statistical asymptotic theory is called singular statistical models. It says that Bayes learning is valid to the singular model. In the Bayes learning, it is important computing average of A posteriori distribution. However, the computation is difficult. Therefore, MCMC method is widely used. In this paper, We introduce the theoretical way to evaluate the correctness of MCMC method. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | singular model / weighted blowup / MCMC |
| Paper # | NC2007-31 |
| Date of Issue |
| Conference Information | |
| Committee | NC |
|---|---|
| Conference Date | 2007/7/17(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | |
| Vice Chair | |
| Secretary | |
| Assistant | |
| Paper Information | |
| Registration To | Neurocomputing (NC) |
|---|---|
| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | On the Computation Approach of Learning Coefficients by Weighted Resolution of Singularities |
| Sub Title (in English) | |
| Keyword(1) | singular model |
| Keyword(2) | weighted blowup |
| Keyword(3) | MCMC |
| 1st Author's Name | Takeshi MATSUDA |
| 1st Author's Affiliation | Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology() |
| 2nd Author's Name | Sumio WATANABE |
| 2nd Author's Affiliation | Precision and Intelligence Laboratory, Tokyo Institute of Technology |
| Date | 2007-07-25 |
| Paper # | NC2007-31 |
| Volume (vol) | vol.107 |
| Number (no) | 157 |
| Page | pp.pp.- |
| #Pages | 5 |
| Date of Issue | |