Presentation 2015-03-17
Latent dynamics estimation from time-series spectral data
Shin MURATA, Kenji NAGATA, Makoto UEMURA, Masato OKADA,
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Abstract(in English) Estimation of latent dynamics from time-series data is important problem in a broad range of fields. In this research, we focused on time-series spectral data, which is obtained in planetary science, condensed matter science or any other field, and its latent dynamics. Center, width and amplitude of each peak reflect the nature of subject. In this research, we proposed a method to estimate the parameters and their latent dynamics, and the number of peaks and the order of the model by using Bayesian inference.
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Keyword(in English) Bayesian inference / model selection / AR model / spectral decomposition / replica exchange Monte Carlo method
Paper # MBE2014-173,NC2014-124
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Committee NC
Conference Date 2015/3/9(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Latent dynamics estimation from time-series spectral data
Sub Title (in English)
Keyword(1) Bayesian inference
Keyword(2) model selection
Keyword(3) AR model
Keyword(4) spectral decomposition
Keyword(5) replica exchange Monte Carlo method
1st Author's Name Shin MURATA
1st Author's Affiliation Graduate School of Frontier Sciences, The University of Tokyo()
2nd Author's Name Kenji NAGATA
2nd Author's Affiliation Graduate School of Frontier Sciences, The University of Tokyo
3rd Author's Name Makoto UEMURA
3rd Author's Affiliation Hiroshima Astrophysical Science Center, Hiroshima University
4th Author's Name Masato OKADA
4th Author's Affiliation Graduate School of Frontier Sciences, The University of Tokyo:RIKEN Brain Science Institute
Date 2015-03-17
Paper # MBE2014-173,NC2014-124
Volume (vol) vol.114
Number (no) 515
Page pp.pp.-
#Pages 6
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