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Divergence Measures

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...

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Natura: Online
Lingua:inglese
Pubblicazione: MDPI - Multidisciplinary Digital Publishing Institute 2022
Soggetti:
Bregman divergence
f-divergence
Jensen–Bregman divergence
Jensen diversity
Jensen–Shannon divergence
capacitory discrimination
Jensen–Shannon centroid
mixture family
information geometry
difference of convex (DC) programming
conditional Rényi divergence
horse betting
Kelly gambling
Rényi divergence
Rényi mutual information
relative entropy
chi-squared divergence
f-divergences
method of types
large deviations
strong data–processing inequalities
information contraction
maximal correlation
Markov chains
information inequalities
mutual information
Rényi entropy
Carlson–Levin inequality
information measures
hypothesis testing
total variation
skew-divergence
convexity
Pinsker’s inequality
Bayes risk
statistical divergences
minimum divergence estimator
maximum likelihood
bootstrap
conditional limit theorem
Bahadur efficiency
α-mutual information
Augustin–Csiszár mutual information
data transmission
error exponents
dimensionality reduction
discriminant analysis
statistical inference
n/a
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
thema EDItEUR::P Mathematics and Science
Accesso online:ONIX_20220621_9783036543321_2
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Riassunto: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 R_ényi 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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