Minimax Rényi Redundancy

Semih Yagli; Yucel Altug; Sergio Verdu

doi:10.1109/TIT.2018.2803070

Minimax Rényi Redundancy

Semih Yagli, Yucel Altug, Sergio Verdu

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

The redundancy for universal lossless compression of discrete memoryless sources in Campbell's setting is characterized as a minimax Rényi divergence, which is shown to be equal to the maximal \alpha -mutual information via a generalized redundancy-capacity theorem. Special attention is placed on the analysis of the asymptotics of minimax Rényi divergence, which is determined up to a term vanishing in blocklength.

Original language	English (US)
Pages (from-to)	3715-3733
Number of pages	19
Journal	IEEE Transactions on Information Theory
Volume	64
Issue number	5
DOIs	https://doi.org/10.1109/TIT.2018.2803070
State	Published - May 2018

All Science Journal Classification (ASJC) codes

Information Systems
Computer Science Applications
Library and Information Sciences

Keywords

Jeffreys' prior
Rényi divergence
Universal lossless compression
generalized redundancy-capacity theorem
minimax redundancy
minimax regret
risk aversion
α-mutual information

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10.1109/TIT.2018.2803070

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AU - Verdu, Sergio

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KW - Jeffreys' prior

KW - Rényi divergence

KW - Universal lossless compression

KW - generalized redundancy-capacity theorem

KW - minimax redundancy

KW - minimax regret

KW - risk aversion

KW - α-mutual information

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Minimax Rényi Redundancy

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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