Divergence Measures: Mathematical Foundations and Applications in Information-Theoretic and Statistical Problems

Igal Sason

doi:10.3390/e24050712

Divergence Measures: Mathematical Foundations and Applications in Information-Theoretic and Statistical Problems

Igal Sason

Research output: Contribution to journal › Editorial

4 Scopus citations

Abstract

Data science, information theory, probability theory, statistical learning, statistical signal processing, and other related disciplines greatly benefit from non-negative measures of dissimilarity between pairs of probability measures [...].

Original language	English
Article number	712
Number of pages	5
Journal	Entropy
Volume	24
Issue number	5
DOIs	https://doi.org/10.3390/e24050712
State	Published - May 2022

ASJC Scopus subject areas

Information Systems
Mathematical Physics
Physics and Astronomy (miscellaneous)
Electrical and Electronic Engineering

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abstract = "Data science, information theory, probability theory, statistical learning, statistical signal processing, and other related disciplines greatly benefit from non-negative measures of dissimilarity between pairs of probability measures [...].",

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Divergence Measures: Mathematical Foundations and Applications in Information-Theoretic and Statistical Problems

Abstract

ASJC Scopus subject areas

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