An entropy estimator based on polynomial regression with poisson error structure

Hideitsu Hino, Shotaro Akaho, Noboru Murata

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

A method for estimating Shannon differential entropy is proposed based on the second order expansion of the probability mass around the inspection point with respect to the distance from the point. Polynomial regression with Poisson error structure is utilized to estimate the values of density function. The density estimates at every given data points are averaged to obtain entropy estimators. The proposed estimator is shown to perform well through numerical experiments for various probability distributions.

本文言語English
ホスト出版物のタイトルNeural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
編集者Seiichi Ozawa, Kazushi Ikeda, Derong Liu, Akira Hirose, Kenji Doya, Minho Lee
出版社Springer Verlag
ページ11-19
ページ数9
ISBN(印刷版)9783319466712
DOI
出版ステータスPublished - 2016
イベント23rd International Conference on Neural Information Processing, ICONIP 2016 - Kyoto, Japan
継続期間: 2016 10 162016 10 21

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
9948 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other23rd International Conference on Neural Information Processing, ICONIP 2016
CountryJapan
CityKyoto
Period16/10/1616/10/21

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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