Computing f‐divergences and distances of high‐dimensional probability density functions

DOI10.1002/NLA.2467OpenAlexW4295847195WikidataQ114235384 ScholiaQ114235384MaRDI QIDQ6133040

Marco Scavino, Hermann G. Matthies, Alessio Spantini, Youssef M. Marzouk, Alexander Litvinenko

Publication date: 17 August 2023

Published in: Numerical Linear Algebra with Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/nla.2467

zbMATH Keywords

Kullback-Leibler divergencecomputational algorithmslow-rank approximationtensor representation\(f\)-divergencehigh-dimensional probability density

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Infinitely divisible distributions; stable distributions (60E07) Computational methods for problems pertaining to statistics (62-08) Probabilistic models, generic numerical methods in probability and statistics (65C20) Computational methods for problems pertaining to probability theory (60-08) Characteristic functions; other transforms (60E10) Multidimensional problems (41A63) Approximations to statistical distributions (nonasymptotic) (62E17) Interpolation in approximation theory (41A05) Measures of information, entropy (94A17) Approximation by arbitrary linear expressions (41A45) Numerical computation of matrix exponential and similar matrix functions (65F60) Numerical methods for low-rank matrix approximation; matrix compression (65F55)

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