Presentation | 2011-03-29 Minimum Variational Free Energy and Average Generalization Error in Latent Variable Models Kazuho WATANABE, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Bayesian learning, widely used in many applied data-modeling problems, is often accomplished with approximation schemes because it requires intractable computation of the posterior distributions. In this study, we focus on the approximation method, variational Bayes, for latent variable models. The asymptotic form of the minimum of the variational free energy, the objective function of variational Bayes, is analyzed and related to the asymptotic theory of Bayesian learning. This analysis additionally implies a relationship between the generalization performance of the variational Bayes approach and the minimum variational free energy. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | variational Bayes / variational free energy / generalization error / asymptotic analysis |
Paper # | IBISML2010-108 |
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Committee | IBISML |
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Conference Date | 2011/3/21(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Information-Based Induction Sciences and Machine Learning (IBISML) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Minimum Variational Free Energy and Average Generalization Error in Latent Variable Models |
Sub Title (in English) | |
Keyword(1) | variational Bayes |
Keyword(2) | variational free energy |
Keyword(3) | generalization error |
Keyword(4) | asymptotic analysis |
1st Author's Name | Kazuho WATANABE |
1st Author's Affiliation | Graduate School of Information Science, Nara Institute of Science and Technology() |
Date | 2011-03-29 |
Paper # | IBISML2010-108 |
Volume (vol) | vol.110 |
Number (no) | 476 |
Page | pp.pp.- |
#Pages | 8 |
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