Universal estimation of information measures for analog sources

Qing Wang, Sanjeev R. Kulkarni, Sergio Verd

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


This monograph presents an overview of universal estimation of information measures for continuous-alphabet sources. Special attention is given to the estimation of mutual information and divergence based on independent and identically distributed (i.i.d.) data. Plug-in methods, partitioning-based algorithms, nearest-neighbor algorithms as well as other approaches are reviewed, with particular focus on consistency, speed of convergence and experimental performance.

Original languageEnglish (US)
Pages (from-to)265-353
Number of pages89
JournalFoundations and Trends in Communications and Information Theory
Issue number3
StatePublished - 2008

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Applied Mathematics


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