Presentation 2012-12-12
Distribution estimation of hyperparameters in Markov random field model
Yoshinori Ohno, Kenji Nagata, Hayaru Shouno, Masato Okada,
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Abstract(in English) Recent advances in measurement techniques allow us to obtain a large quantity of imaging data in various natural science fields. These data can be analyzed by Markov random field (MRF) models which have made progress in information science. We can estimate hyperparameters in our MRF model, which is a probabilistic one, by the framework of Bayesian inference. When our MRF model is applied to diffusion systems which often appear in the natural sciences, a hyperparameter is an important parameter which corresponds to the diffusion coefficient. Thus, in this study, we calculate not only a point estimate but also a distribution estimate of hyperparameters to evaluate estimation reliability. Besides, we discuss the dependence of estimation accuracy and reliability on the number of pixels in imaging data.
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Keyword(in English) Markov random field model / hyperparameter / distribution estimation
Paper # NC2012-86
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Committee NC
Conference Date 2012/12/5(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Distribution estimation of hyperparameters in Markov random field model
Sub Title (in English)
Keyword(1) Markov random field model
Keyword(2) hyperparameter
Keyword(3) distribution estimation
1st Author's Name Yoshinori Ohno
1st Author's Affiliation The University of Tokyo()
2nd Author's Name Kenji Nagata
2nd Author's Affiliation The University of Tokyo
3rd Author's Name Hayaru Shouno
3rd Author's Affiliation The University of Electro-Communications
4th Author's Name Masato Okada
4th Author's Affiliation The University of Tokyo/RIKEN
Date 2012-12-12
Paper # NC2012-86
Volume (vol) vol.112
Number (no) 345
Page pp.pp.-
#Pages 6
Date of Issue