Universal estimation of erasure entropy

Jiming Yu, Sergio Verdú

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Erasure entropy rate differs from Shannon's entropy rate in that the conditioning occurs with respect to both the past and the future, as opposed to only the past (or the future). In this paper, consistent universal algorithms for estimating erasure entropy rate are proposed based on the basic and extended context-tree weighting (CTW) algorithms. Simulation results for those algorithms applied to Markov sources, tree sources, and English texts are compared to those obtained by fixed-order plug-in estimators with different orders.

Original languageEnglish (US)
Pages (from-to)350-357
Number of pages8
JournalIEEE Transactions on Information Theory
Issue number1
StatePublished - 2009

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Library and Information Sciences
  • Computer Science Applications


  • Bidirectional context tree
  • Context-tree weighting
  • Data compression
  • Entropy rate
  • Universal algorithms
  • Universal modeling


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