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Divergence Measures: Mathematical Foundations and Applications in Information-Theoretic and Statistical Problems
Hardcover
ISBN13: 9783036543321
Publisher: Mdpi Ag
Published: Jun 1 2022
Pages: 256
Weight: 1.66
Height: 0.81 Width: 6.69 Depth: 9.61
Language: English
Data science, information theory, probability theory, statistical learning and other related disciplines greatly benefit from non-negative measures of dissimilarity between pairs of probability measures. These are known as divergence measures, and exploring their mathematical foundations and diverse applications is of significant interest. The present Special Issue, entitled Divergence Measures: Mathematical Foundations and Applications in Information-Theoretic and Statistical Problems, includes eight original contributions, and it is focused on the study of the mathematical properties and applications of classical and generalized divergence measures from an information-theoretic perspective. It mainly deals with two key generalizations of the relative entropy: namely, the Rényi divergence and the important class of f -divergences. It is our hope that the readers will find interest in this Special Issue, which will stimulate further research in the study of the mathematical foundations and applications of divergence measures.
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