Asymptotic analysis of approximate Bayesian inference methods
| Project/Area Number |
20800012
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| Research Category |
Grant-in-Aid for Young Scientists (Start-up)
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| Allocation Type | Single-year Grants |
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| Research Field |
Intelligent informatics
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| Research Institution | The University of Tokyo |
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Principal Investigator |
WATANABE Kazuho The University of Tokyo, 情報科学研究科, 助教 (10506744)
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| Project Period (FY) |
2008 – 2009
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| Project Status |
Completed (Fiscal Year 2009)
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| Budget Amount *help |
¥3,042,000 (Direct Cost: ¥2,340,000、Indirect Cost: ¥702,000)
Fiscal Year 2009: ¥1,456,000 (Direct Cost: ¥1,120,000、Indirect Cost: ¥336,000)
Fiscal Year 2008: ¥1,586,000 (Direct Cost: ¥1,220,000、Indirect Cost: ¥366,000)
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| Keywords | ベイズ推定 / 変分ベイズ法 / 局所変分近似 / 混合モデル / 変動二項過程 / 情報幾何 / 階層ベイズ法 / 指数型分布 / クラスタリング / 次元圧縮 / 発火率推定 |
|---|
| Research Abstract |
We developed an approximate inference method for the constrained exponential-family mixture models that are used for the simultaneous dimensionality reduction and clustering of high-dimensional data. It was applied to the hand-written digit recognition task and its effectiveness was demonstrated. We also derived an approximate inference method for the varying binomial process that efficiently estimates the varying probabilities of some event. Furthermore, we demonstrated the general framework and the information-theoretic view of the local variational approximation.
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Report
(3 results)
Research Products
(26 results)
