Weighted entropy and its use in computer science and beyond

Mark Kelbert, Izabella Stuhl, Yuri Suhov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The concept of weighted entropy takes into account values of different outcomes, i.e., makes entropy context-dependent, through the weight function. We analyse analogs of the Fisher information inequality and entropy-power inequality for the weighted entropy and discuss connections with weighted Lieb’s splitting inequality. The concepts of rates of the weighted entropy and information are also discussed.

Original languageEnglish (US)
Title of host publicationAnalytical and Computational Methods in Probability Theory - 1st International Conference, ACMPT 2017, Proceedings
EditorsVladimir V. Rykov, Nozer D. Singpurwalla, Andrey M. Zubkov
PublisherSpringer Verlag
Pages293-308
Number of pages16
ISBN (Print)9783319715032
DOIs
StatePublished - Jan 1 2017
Event1st International Conference Analytical and Computational Methods in Probability Theory, ACMPT 2017 - Moscow, Russian Federation
Duration: Oct 23 2017Oct 27 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10684 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Conference Analytical and Computational Methods in Probability Theory, ACMPT 2017
CountryRussian Federation
CityMoscow
Period10/23/1710/27/17

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Kelbert, M., Stuhl, I., & Suhov, Y. (2017). Weighted entropy and its use in computer science and beyond. In V. V. Rykov, N. D. Singpurwalla, & A. M. Zubkov (Eds.), Analytical and Computational Methods in Probability Theory - 1st International Conference, ACMPT 2017, Proceedings (pp. 293-308). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10684 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-71504-9_25