Minimax Renyi Redundancy

被引：4

作者：

Yagli, Semih ^{[1
]}

Altug, Yucel ^{[2
]}

Verdu, Sergio ^{[1
]}

机构：

[1] Princeton Univ, Dept Elect Engn, Princeton, NJ 08544 USA

[2] Natera Inc, San Carlos, CA 94070 USA

来源：

IEEE TRANSACTIONS ON INFORMATION THEORY | 2018年 / 64卷 / 05期

基金：

美国国家科学基金会;

关键词：

Universal lossless compression; generalized redundancy-capacity theorem; minimax redundancy; minimax regret; Jeffreys' prior; risk aversion; Renyi divergence; alpha-mutual information; DATA-COMPRESSION; RISK-AVERSION; INFORMATION; ASYMPTOTICS; STRATEGIES;

D O I：

10.1109/TIT.2018.2803070

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The redundancy for universal lossless compression of discrete memoryless sources in Campbell's setting is characterized as a minimax Renyi 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 Renyi divergence, which is determined up to a term vanishing in blocklength.

引用

页码：3715 / 3733

页数：19

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