Extremal distributions in information theory and hypothesis testing

Charuhas Pandit, Jianyi Huang, Sean Meyn, Venugopal Varadachari Veeravalli

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

Abstract

Hypothesis testing was performed in order to compute channel capacity in information theory. Two general classes of optimization such as convex and linear programs were considered, and constrained set was defined by a finite number of moment constraints. It was assumed that the constraints were such that each π ∈P was a probability distribution, and equality constraints were also considered using the same notation. It was possible to obtain worst-case bounds on the probability of a given set, and overall probability distributions in a given moment class.

Original languageEnglish (US)
Title of host publication2004 IEEE Information Theory Workshop - Proceedings, ITW
Pages76-81
Number of pages6
StatePublished - Dec 1 2004
Event2004 IEEE Information Theory Workshop - Proceedings, ITW - San Antonio, TX, United States
Duration: Oct 24 2004Oct 29 2004

Publication series

Name2004 IEEE Information Theory Workshop - Proceedings, ITW

Other

Other2004 IEEE Information Theory Workshop - Proceedings, ITW
CountryUnited States
CitySan Antonio, TX
Period10/24/0410/29/04

ASJC Scopus subject areas

  • Engineering(all)

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