Universal estimation of entropy and divergence via block sorting

Haixiao Cai, Sanjeev R. Kulkarni, Sergio Verdú

Research output: Contribution to journalConference article

6 Scopus citations

Abstract

A new algorithm to estimate both entropy and divergence of two finite-alphabet, finite memory tree sources, using information provided by a realization from each of the two sources was presented. The algorithm outperforms a previous LZ-based method. It is motivated by data compression based on the Burrows-Wheeler Block Sorting Transform.

Original languageEnglish (US)
Number of pages1
JournalIEEE International Symposium on Information Theory - Proceedings
StatePublished - Sep 12 2002
Event2002 IEEE International Symposium on Information Theory - Lausanne, Switzerland
Duration: Jun 30 2002Jul 5 2002

All Science Journal Classification (ASJC) codes

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

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