Asymptotics of Bayesian estimation for nested models under misspecification

Nozomi Miya*, Tota Suko, Goki Yasuda, Toshiyasu Matsushima

*この研究の対応する著者

研究成果: Conference contribution

抄録

We analyze the asymptotic properties of the cumulative logarithmic loss in the decision problem based on the Bayesian principle and explicitly identify the constant terms of the asymptotic equations as in the case of previous studies by Clarke and Barron and Gotoh et al. We assume that the set of models is given that identify a class of parameterized distributions, it has a nested structure and the source distribution is not contained in all the families of parameterized distributions that are identified by each model. The cumulative logarithmic loss is the sum of the logarithmic loss functions for each time decision -, e.g., the redundancy in the universal noiseless source coding.

本文言語English
ホスト出版物のタイトル2012 International Symposium on Information Theory and Its Applications, ISITA 2012
ページ86-90
ページ数5
出版ステータスPublished - 2012 12 1
イベント2012 International Symposium on Information Theory and Its Applications, ISITA 2012 - Honolulu, HI, United States
継続期間: 2012 10 282012 10 31

出版物シリーズ

名前2012 International Symposium on Information Theory and Its Applications, ISITA 2012

Conference

Conference2012 International Symposium on Information Theory and Its Applications, ISITA 2012
国/地域United States
CityHonolulu, HI
Period12/10/2812/10/31

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 情報システム

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