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Cited by 420

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Information Theory and Statistics: A Tutorial

Csiszár, Imre, Shields, Paul C.·2004·Foundations and Trends™ in Communications and Information Theory

Abstract

This tutorial is concerned with applications of information theory concepts in statistics, in the finite alphabet setting. The information measure known as information divergence or Kullback-Leibler distance or relative entropy plays a key role, often with a geometric flavor as an analogue of squared Euclidean distance, as in the concepts of I-projection, I-radius and I-centroid. The topics covered include large deviations, hypothesis testing, maximum likelihood estimation in exponential families, analysis of contingency tables, and iterative algorithms with an “information geometry” background. Also, an introduction is provided to the theory of universal coding, and to statistical inference via the minimum description length principle motivated by that theory.

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Metadata

Authors
Csiszár, I., & Shields, P. C.
Venue
Foundations and Trends™ in Communications and Information Theory
Publisher
Emerald
Published
2004
Volume
1
Issue
4
Pages
417-528
ISSN
  • 1567-2190 (ISSN-L)
  • 1567-2328
OpenAlex
W4293404332
Cited by
420
References
22
FWCI
2.62
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Information Theory and Statistics: A Tutorial. https://app.cochat.ai/papers/10.1561/0100000004
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Csiszár, I., & Shields, P. C. (2004). Information Theory and Statistics: A Tutorial. Foundations and Trends™ in Communications and Information Theory. https://doi.org/10.1561/0100000004

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