Improved Bounds on Guessing Moments via Rényi Measures

Igal Sason, Sergio Verdu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper provides upper and lower bounds on the optimal guessing moments of a random variable taking values on a finite set when side information may be available. These moments quantify the number of guesses required for correctly identifying the unknown object and, similarly to Arikan's bounds, they are expressed in terms of the Arimoto- Rényi conditional entropy. Although Arikan's bounds are asymptotically tight, the improvement of the bounds in this paper is significant in the non-asymptotic regime. Relationships between moments of the optimal guessing function and the MAP error probability are provided, characterizing the exact locus of their attainable values.

Original languageEnglish
Title of host publication2018 IEEE International Symposium on Information Theory, ISIT 2018
Pages566-570
Number of pages5
DOIs
StatePublished - 15 Aug 2018
Event2018 IEEE International Symposium on Information Theory, ISIT 2018 - Vail, United States
Duration: 17 Jun 201822 Jun 2018

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2018-June

Conference

Conference2018 IEEE International Symposium on Information Theory, ISIT 2018
Country/TerritoryUnited States
CityVail
Period17/06/1822/06/18

Keywords

  • Error probability
  • Guessing moments
  • M-ary hypothesis testing
  • MAP decision rules
  • Rényi information measures

All Science Journal Classification (ASJC) codes

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

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