2009 Fiscal Year Final Research Report
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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| 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 |
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| 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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Research Products
(10 results)
