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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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2012: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2011: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2010: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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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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Report
(4 results)
Research Products
(67 results)
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[Presentation] ベイズ推定によるスペクトル分解2012
Author(s)
永田賢二
Organizer
第一回東大新領域・KEK 連携教育シンポジウム
Place of Presentation
高エネルギー加速器研究機構(KEK), 茨城
Year and Date
2012-09-11
Related Report
Invited
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