Presentation | 2006-10-11 Bayesian Hypothesis Testing in Singular Models; a Case Study of Time Series Analysis Kaori FUJIWARA, Sumio WATANABE, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Statistical hypothesis testing is constructed by comparing the probability of an alternative hypothesis with that of a null hypothesis. The log likelihood ratio (LR) using the maximum likelihood estimator MLE is not appropriate for testing singular models, because LR using MLE diverges in singular models resulting that it gives weak hypothesis testing. In this paper, based on algebraic geometrical method, we theoretically derive the asymptotic distribution of the Bayesian log likelihood, which show that the Bayesian testing hypothesis is appropriate for singular learning machines. The proposed method is applied to the problem of change point detection, which is a typical singular learning machine. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | singular learning machine / Bayes hypothesis testing / Bayes marginal likelihood ratio / Bayes factor / change-point detection |
Paper # | NC2006-51 |
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Committee | NC |
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Conference Date | 2006/10/4(1days) |
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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) | Bayesian Hypothesis Testing in Singular Models; a Case Study of Time Series Analysis |
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Keyword(1) | singular learning machine |
Keyword(2) | Bayes hypothesis testing |
Keyword(3) | Bayes marginal likelihood ratio |
Keyword(4) | Bayes factor |
Keyword(5) | change-point detection |
1st Author's Name | Kaori FUJIWARA |
1st Author's Affiliation | Tokyo Research Lab., IBM Japan Ltd.() |
2nd Author's Name | Sumio WATANABE |
2nd Author's Affiliation | PI Lab., Tokyo Institute of Technology |
Date | 2006-10-11 |
Paper # | NC2006-51 |
Volume (vol) | vol.106 |
Number (no) | 279 |
Page | pp.pp.- |
#Pages | 5 |
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