Estimation of entropy rate and Rényi entropy rate for Markov chains

Sudeep Kamath, Sergio Verdu

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

6 Scopus citations

Abstract

Estimation of the entropy rate of a stochastic process with unknown statistics, from a single sample path is a classical problem in information theory. While universal estimators for general families of processes exist, the estimates have not been accompanied by guarantees for fixed-length sample paths. We provide finite sample bounds on the convergence of a plug-in type estimator for the entropy rate of a Markov chain in terms of its alphabet size and its mixing properties. We also discuss Rényi entropy rate estimation for reversible Markov chains.

Original languageEnglish (US)
Title of host publicationProceedings - ISIT 2016; 2016 IEEE International Symposium on Information Theory
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages685-689
Number of pages5
ISBN (Electronic)9781509018062
DOIs
StatePublished - Aug 10 2016
Event2016 IEEE International Symposium on Information Theory, ISIT 2016 - Barcelona, Spain
Duration: Jul 10 2016Jul 15 2016

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2016-August
ISSN (Print)2157-8095

Other

Other2016 IEEE International Symposium on Information Theory, ISIT 2016
CountrySpain
CityBarcelona
Period7/10/167/15/16

All Science Journal Classification (ASJC) codes

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

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