Information contraction and decomposition

Information contraction is one of the most fundamental concepts in information theory as evidenced by the numerous classical converse theorems that utilize it. In this dissertation, we study several problems aimed at better understanding this notion, broadly construed, within the intertwined realms of information theory, statistics, and discrete probability theory. In information theory, the contraction of f-divergences, such as Kullback-Leibler (KL) divergence, X²-divergence, and total variation (TV) distance, through channels (or the contraction of mutual f-information along Markov chains) is quantitatively captured by the well-known data processing inequalities.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Makur, Anuran.

Advisor dc:contributor.advisor

• Lizhong Zheng and Yury Polyanskiy.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122692

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122692