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

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

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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Bibliographic Details
Format: eBook
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
Subjects:
Mathematics & science
Research & information: general
Augustin-Csiszár mutual information
Bahadur efficiency
Bayes risk
bootstrap
Bregman divergence
capacitory discrimination
Carlson-Levin inequality
chi-squared divergence
conditional limit theorem
conditional Rényi divergence
convexity
data transmission
difference of convex (DC) programming
dimensionality reduction
discriminant analysis
error exponents
f-divergence
f-divergences
horse betting
hypothesis testing
information contraction
information geometry
information inequalities
information measures
Jensen diversity
Jensen-Bregman divergence
Jensen-Shannon centroid
Jensen-Shannon divergence
Kelly gambling
large deviations
Markov chains
maximal correlation
maximum likelihood
method of types
minimum divergence estimator
mixture family
mutual information
n/a
Pinsker's inequality
relative entropy
Rényi divergence
Rényi entropy
Rényi mutual information
skew-divergence
statistical divergences
statistical inference
strong data-processing inequalities
total variation
α-mutual information
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