論文

査読有り
2014年12月

Asymptotics of Bayesian Inference for a Class of Probabilistic Models under Misspecification

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES
  • Nozomi Miya
  • ,
  • Tota Suko
  • ,
  • Goki Yasuda
  • ,
  • Toshiyasu Matsushima

E97-A
12
開始ページ
2352
終了ページ
2360
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1587/transfun.E97.A.2352
出版者・発行元
IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG

In this paper, sequential prediction is studied. The typical assumptions about the probabilistic model in sequential prediction are following two cases. One is the case that a certain probabilistic model is given and the parameters are unknown. The other is the case that not a certain probabilistic model but a class of probabilistic models is given and the parameters are unknown. If there exist some parameters and some models such that the distributions that are identified by them equal the source distribution, an assumed model or a class of models can represent the source distribution. This case is called that specifiable condition is satisfied. In this study, the decision based on the Bayesian principle is made for a class of probabilistic models (not for a certain probabilistic model). The case that specifiable condition is not satisfied is studied. Then, the asymptotic behaviors of the cumulative logarithmic loss for individual sequence in the sense of almost sure convergence and the expected loss, i.e. redundancy are analyzed and the constant terms of the asymptotic equations are identified.

リンク情報
DOI
https://doi.org/10.1587/transfun.E97.A.2352
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000351567100009&DestApp=WOS_CPL
ID情報
  • DOI : 10.1587/transfun.E97.A.2352
  • ISSN : 1745-1337
  • Web of Science ID : WOS:000351567100009

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