Evaluation of Error Probability of Classification Based on the Analysis of the Bayes Code: Extension and Example

研究成果: Conference contribution

抄録

Suppose that we have two training sequences generated by parametrized distributions P_{\theta} and P_{\varepsilon^{*}}, where \theta ∗ and \xi^{*} are unknown true parameters. Given training sequences, we study the problem of classifying whether a test sequence was generated according to P_{\theta} ∗ or P_{\xi^{*}}. This problem can be thought of as a hypothesis testing problem and our aim is to analyze the weighted sum of type-I and type-II error probabilities. Utilizing the analysis of the codeword lengths of the Bayes code, our previous study derived more refined bounds on the error probability than known previously. However, our previous study had the following deficiencies: i) the prior distributions of \theta and \xi are the same; ii) the prior distributions of two hypotheses are uniform; iii) no numerical calculation at finite blocklength. This study solves these problems. We remove the restrictions i) and ii) and derive more general results than obtained previously. To deal with problem iii), we perform a numerical calculation for a concrete model.

本文言語English
ホスト出版物のタイトル2021 IEEE International Symposium on Information Theory, ISIT 2021 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1445-1450
ページ数6
ISBN(電子版)9781538682098
DOI
出版ステータスPublished - 2021 7 12
イベント2021 IEEE International Symposium on Information Theory, ISIT 2021 - Virtual, Melbourne, Australia
継続期間: 2021 7 122021 7 20

出版物シリーズ

名前IEEE International Symposium on Information Theory - Proceedings
2021-July
ISSN(印刷版)2157-8095

Conference

Conference2021 IEEE International Symposium on Information Theory, ISIT 2021
国/地域Australia
CityVirtual, Melbourne
Period21/7/1221/7/20

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • 情報システム
  • モデリングとシミュレーション
  • 応用数学

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