2013 Fiscal Year Final Research Report
General-purpose use of Bayesian learning for hierarchical probabilistic models
| Project/Area Number |
22700230
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | The University of Tokyo |
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Principal Investigator |
NAGATA Kenji 東京大学, 新領域創成科学研究科, 助教 (10556062)
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| Project Period (FY) |
2010-04-01 – 2014-03-31
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| Keywords | 機械学習 / ベイズ学習 / 交換モンテカルロ法 / メトロポリス法 / スペクトル分解 |
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| Research Abstract |
The purpose of this project is to establish a general-purpose use of Bayesian learning for hierarchical probabilistic model such as a neural network and a hidden Markov model. We use an exchange Monte Carlo method for performing Bayesian learning efficiently, and significantly improve the computational cost by parallelizing the exhcnage Monte Carlo method. Moreover, we construct a optimal design of the exchange Monte Carlo method and apply the proposed method to the spectral deconvolution for the radial basis function networks.
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