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

Dear Colleagues,

Information theory and probability, statistical learning theory, statistical signal processing, and other related disciplines greatly benefit from non-negative measures of dissimilarity (i.e., divergence measures) between pairs of probability measures defined on the same measurable space. Exploring the mathematical foundations of divergence measures (e.g., Bregman, Renyi, and f-divergences), and their potential applications in new information-theoretic and statistical problems is of interest, and many interesting results involve the use of these generalized divergence measures.

This Special Issue encourages research and survey papers on the mathematical properties and applications of divergence measures from an information-theoretic perspective.

Prof. Igal Sason
Guest Editor

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