Asymptotically minimax regret for models with hidden variables

Junnichi Takeuchi, Andrew R. Barron

研究成果: 著書/レポートタイプへの貢献会議での発言

3 引用 (Scopus)

抄録

We study the problems of data compression, gambling and prediction of a string xn = x1x2...xn from an alphabet X, in terms of regret with respect to models with hidden variables including general mixture families. When the target class is a non-exponential family, a modification of Jeffreys prior which has measure outside the given family of densities was introduced to achieve the minimax regret [8], under certain regularity conditions. In this paper, we show that the models with hidden variables satisfy those regularity conditions, when the hidden variables' model is an exponential family. In paticular, we do not have to restrict the class of data strings so that the MLE is in the interior of the parameter space for the case of the general mixture family.

元の言語英語
ホスト出版物のタイトル2014 IEEE International Symposium on Information Theory, ISIT 2014
出版者Institute of Electrical and Electronics Engineers Inc.
ページ3037-3041
ページ数5
ISBN(印刷物)9781479951864
DOI
出版物ステータス出版済み - 1 1 2014
イベント2014 IEEE International Symposium on Information Theory, ISIT 2014 - Honolulu, HI, 米国
継続期間: 6 29 20147 4 2014

出版物シリーズ

名前IEEE International Symposium on Information Theory - Proceedings
ISSN(印刷物)2157-8095

その他

その他2014 IEEE International Symposium on Information Theory, ISIT 2014
米国
Honolulu, HI
期間6/29/147/4/14

Fingerprint

Hidden Variables
Regret
Minimax
Regularity Conditions
Strings
Maximum likelihood estimation
Data compression
Gambling
Jeffreys Prior
Exponential Family
Data Compression
Model
Parameter Space
Interior
Target
Family
Prediction
Class

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Information Systems
  • Modelling and Simulation
  • Applied Mathematics

これを引用

Takeuchi, J., & Barron, A. R. (2014). Asymptotically minimax regret for models with hidden variables. : 2014 IEEE International Symposium on Information Theory, ISIT 2014 (pp. 3037-3041). [6875392] (IEEE International Symposium on Information Theory - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIT.2014.6875392

Asymptotically minimax regret for models with hidden variables. / Takeuchi, Junnichi; Barron, Andrew R.

2014 IEEE International Symposium on Information Theory, ISIT 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 3037-3041 6875392 (IEEE International Symposium on Information Theory - Proceedings).

研究成果: 著書/レポートタイプへの貢献会議での発言

Takeuchi, J & Barron, AR 2014, Asymptotically minimax regret for models with hidden variables. : 2014 IEEE International Symposium on Information Theory, ISIT 2014., 6875392, IEEE International Symposium on Information Theory - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 3037-3041, 2014 IEEE International Symposium on Information Theory, ISIT 2014, Honolulu, HI, 米国, 6/29/14. https://doi.org/10.1109/ISIT.2014.6875392
Takeuchi J, Barron AR. Asymptotically minimax regret for models with hidden variables. : 2014 IEEE International Symposium on Information Theory, ISIT 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 3037-3041. 6875392. (IEEE International Symposium on Information Theory - Proceedings). https://doi.org/10.1109/ISIT.2014.6875392
Takeuchi, Junnichi ; Barron, Andrew R. / Asymptotically minimax regret for models with hidden variables. 2014 IEEE International Symposium on Information Theory, ISIT 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 3037-3041 (IEEE International Symposium on Information Theory - Proceedings).
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