Universal estimation of information measures

Sergio Verdú

doi:10.1109/ITW.2005.1531895

Universal estimation of information measures

Sergio Verdú

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Scopus citations

Abstract

In this presentation I will give an overview of the state of the art in universal estimation of: Entropy Divergence Mutual Information with emphasis on recent algorithms we have proposed with H. Cai, S. Kulkarni and Q. Wang. These algorithms converge to the desired quantities without any knowledge of the statistical properties of the observed data, under several conditions such as stationary-ergodicity in the case of discrete processes, and memorylessness in the case of analog data. A sampling of the literature in this topic is given below.

Original language	English (US)
Title of host publication	Proceedings of the IEEE ITSOC Information Theory Workshop 2005 on Coding and Complexity, ITW2005
Pages	232
Number of pages	1
DOIs	https://doi.org/10.1109/ITW.2005.1531895
State	Published - 2005
Event	IEEE ITSOC Information Theory Workshop 2005 on Coding and Complexity, ITW2005 - Rotorua, New Zealand Duration: Aug 29 2005 → Sep 1 2005

Publication series

Name	Proceedings of the IEEE ITSOC Information Theory Workshop 2005 on Coding and Complexity, ITW2005

Other

Other	IEEE ITSOC Information Theory Workshop 2005 on Coding and Complexity, ITW2005
Country/Territory	New Zealand
City	Rotorua
Period	8/29/05 → 9/1/05

All Science Journal Classification (ASJC) codes

General Engineering

Access to Document

10.1109/ITW.2005.1531895

Cite this

@inproceedings{148e831022354c49a7993cf6e1ca6f78,

title = "Universal estimation of information measures",

abstract = "In this presentation I will give an overview of the state of the art in universal estimation of: Entropy Divergence Mutual Information with emphasis on recent algorithms we have proposed with H. Cai, S. Kulkarni and Q. Wang. These algorithms converge to the desired quantities without any knowledge of the statistical properties of the observed data, under several conditions such as stationary-ergodicity in the case of discrete processes, and memorylessness in the case of analog data. A sampling of the literature in this topic is given below.",

author = "Sergio Verd{\'u}",

year = "2005",

doi = "10.1109/ITW.2005.1531895",

language = "English (US)",

isbn = "078039481X",

series = "Proceedings of the IEEE ITSOC Information Theory Workshop 2005 on Coding and Complexity, ITW2005",

pages = "232",

booktitle = "Proceedings of the IEEE ITSOC Information Theory Workshop 2005 on Coding and Complexity, ITW2005",

note = "IEEE ITSOC Information Theory Workshop 2005 on Coding and Complexity, ITW2005 ; Conference date: 29-08-2005 Through 01-09-2005",

}

Verdú, S 2005, Universal estimation of information measures. in Proceedings of the IEEE ITSOC Information Theory Workshop 2005 on Coding and Complexity, ITW2005., 1531895, Proceedings of the IEEE ITSOC Information Theory Workshop 2005 on Coding and Complexity, ITW2005, pp. 232, IEEE ITSOC Information Theory Workshop 2005 on Coding and Complexity, ITW2005, Rotorua, New Zealand, 8/29/05. https://doi.org/10.1109/ITW.2005.1531895

Universal estimation of information measures. / Verdú, Sergio.
Proceedings of the IEEE ITSOC Information Theory Workshop 2005 on Coding and Complexity, ITW2005. 2005. p. 232 1531895 (Proceedings of the IEEE ITSOC Information Theory Workshop 2005 on Coding and Complexity, ITW2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Universal estimation of information measures

AU - Verdú, Sergio

PY - 2005

Y1 - 2005

N2 - In this presentation I will give an overview of the state of the art in universal estimation of: Entropy Divergence Mutual Information with emphasis on recent algorithms we have proposed with H. Cai, S. Kulkarni and Q. Wang. These algorithms converge to the desired quantities without any knowledge of the statistical properties of the observed data, under several conditions such as stationary-ergodicity in the case of discrete processes, and memorylessness in the case of analog data. A sampling of the literature in this topic is given below.

AB - In this presentation I will give an overview of the state of the art in universal estimation of: Entropy Divergence Mutual Information with emphasis on recent algorithms we have proposed with H. Cai, S. Kulkarni and Q. Wang. These algorithms converge to the desired quantities without any knowledge of the statistical properties of the observed data, under several conditions such as stationary-ergodicity in the case of discrete processes, and memorylessness in the case of analog data. A sampling of the literature in this topic is given below.

UR - http://www.scopus.com/inward/record.url?scp=33749075209&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33749075209&partnerID=8YFLogxK

U2 - 10.1109/ITW.2005.1531895

DO - 10.1109/ITW.2005.1531895

M3 - Conference contribution

AN - SCOPUS:33749075209

SN - 078039481X

SN - 9780780394810

T3 - Proceedings of the IEEE ITSOC Information Theory Workshop 2005 on Coding and Complexity, ITW2005

SP - 232

BT - Proceedings of the IEEE ITSOC Information Theory Workshop 2005 on Coding and Complexity, ITW2005

T2 - IEEE ITSOC Information Theory Workshop 2005 on Coding and Complexity, ITW2005

Y2 - 29 August 2005 through 1 September 2005

ER -

Universal estimation of information measures

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this