FR2.R4.2

Properties of the Strong Data Processing Constant for Rényi Divergence

Lifu Jin, École Polytechnique Fédérale de Lausanne, Switzerland; Amedeo Roberto Esposito, Institute of Science and Technology Austria, Austria; Michael Gastpar, École Polytechnique Fédérale de Lausanne, Switzerland
Session:
Information Inequalities 1
Track:
9: Shannon Theory
Location:
Omikron II
Presentation Time:
Fri, 12 Jul, 11:50 - 12:10
Session Chair:
Lampros Gavalakis, Gustave Eiffel University
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Session FR2.R4
FR2.R4.1: Proving Information Inequalities by Gaussian Elimination
Laigang Guo, Beijing Normal University, China; Raymond Yeung, The Chinese University of Hong Kong, China; Xiao-Shan Gao, Chinese Academy of Sciences, China
FR2.R4.2: Properties of the Strong Data Processing Constant for Rényi Divergence
Lifu Jin, École Polytechnique Fédérale de Lausanne, Switzerland; Amedeo Roberto Esposito, Institute of Science and Technology Austria, Austria; Michael Gastpar, École Polytechnique Fédérale de Lausanne, Switzerland
FR2.R4.3: Self Improvement of the McEliece-Yu Inequality
Andrei Tanasescu, Pantelimon-George Popescu, University Politehnica of Bucharest, Romania
FR2.R4.4: A Poisson Decomposition for Information and the Information-Event Diagram
Cheuk Ting Li, The Chinese University of Hong Kong, Hong Kong SAR of China
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