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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Bibliografiset tiedot
Aineistotyyppi:	Online
Kieli:	englanti
Julkaistu:	MDPI - Multidisciplinary Digital Publishing Institute 2022
Aiheet:	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
Linkit:	ONIX_20220621_9783036543321_2
Tagit:	Lisää tagi Ei tageja, Lisää ensimmäinen tagi!

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Divergence Measures

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