Presentation | 2011-03-08 Comparison between the Parameter and the Hidden Variable Space for Calculation of the Marginal Likelihood Takushi MIKI, Keisuke YAMAZAKI, Sumio WATANABE, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | The marginal likelihood has important information for model selection and optimization of a prior distribution. In practical situations, a numerical calculation based on the Markov Chain Monte Carlo(MCMC) method is necessary because the analytical calculation is not straightforward in hierarchical learning modes. There are two MCMC methods; one is on the parameter space and the other is on the hidden variable space. However, it has not been studied yet how the space difference affects the numerical accuracy. In this paper, we calculate the marginal likelihood of a mixture of normal distributions using the methods, and compare the two spaces. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Marginal likelihood / Markov Chain Monte Carlo method / mixture of normal distributions / hidden variable |
Paper # | NC2010-176 |
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Committee | NC |
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Conference Date | 2011/2/28(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Comparison between the Parameter and the Hidden Variable Space for Calculation of the Marginal Likelihood |
Sub Title (in English) | |
Keyword(1) | Marginal likelihood |
Keyword(2) | Markov Chain Monte Carlo method |
Keyword(3) | mixture of normal distributions |
Keyword(4) | hidden variable |
1st Author's Name | Takushi MIKI |
1st Author's Affiliation | Department of Computer Science, Tokyo Tech.() |
2nd Author's Name | Keisuke YAMAZAKI |
2nd Author's Affiliation | PI Lab, Tokyo tech. |
3rd Author's Name | Sumio WATANABE |
3rd Author's Affiliation | PI Lab, Tokyo tech. |
Date | 2011-03-08 |
Paper # | NC2010-176 |
Volume (vol) | vol.110 |
Number (no) | 461 |
Page | pp.pp.- |
#Pages | 6 |
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