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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Detalles Bibliográficos
Otros Autores:	Sason, Igal (Editor)
Formato:	Libro electrónico
Idioma:	Inglés
Publicado:	Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
Materias:	Research & information: general Mathematics & science 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
Ver en Biblioteca Universitat Ramon Llull:	https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009666906306719