Presentation 2010-01-18
Rigorous Calculation Method of Bayes Marginal Likelihood in Normal Mixture
Tetsutaro YAMADA, Keisuke YAMAZAKI, Sumio WATANABE,
PDF Download Page PDF download Page Link
Abstract(in Japanese) (See Japanese page)
Abstract(in English) The Bayes marginal likelihood is one of the most important observables in Bayes learning. Several methods have been proposed and studied, for example, Markov chain Monte Carlo, mean field approximation, algebraic geometrical theory, and so on. However, it has been difficult to obtain the rigorous value of the Bayes marginal likelihood. In this paper, we propose a new method to calculate the rigorous Bayes margianl likelihood in normal mixture, which enables us to establish the foundation to measure how accurate several approximation methods are, and experimentally clarify its effectiveness.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Bayes marginal likelihood / rigorous calculation method / normal mixture
Paper # NC2009-76
Date of Issue

Conference Information
Committee NC
Conference Date 2010/1/11(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair
Vice Chair
Secretary
Assistant

Paper Information
Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Rigorous Calculation Method of Bayes Marginal Likelihood in Normal Mixture
Sub Title (in English)
Keyword(1) Bayes marginal likelihood
Keyword(2) rigorous calculation method
Keyword(3) normal mixture
1st Author's Name Tetsutaro YAMADA
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 2010-01-18
Paper # NC2009-76
Volume (vol) vol.109
Number (no) 363
Page pp.pp.-
#Pages 6
Date of Issue