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High-Dimensional Probability: An Introduction with Applications in Data Science by Roman Vershynin (Hardcover, 2018)
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About this product
Product Information
High-dimensional probability offers insight into the behavior of random vectors, random matrices, random subspaces, and objects used to quantify uncertainty in high dimensions. Drawing on ideas from probability, analysis, and geometry, it lends itself to applications in mathematics, statistics, theoretical computer science, signal processing, optimization, and more. It is the first to integrate theory, key tools, and modern applications of high-dimensional probability. Concentration inequalities form the core, and it covers both classical results such as Hoeffding's and Chernoff's inequalities and modern developments such as the matrix Bernstein's inequality. It then introduces the powerful methods based on stochastic processes, including such tools as Slepian's, Sudakov's, and Dudley's inequalities, as well as generic chaining and bounds based on VC dimension. A broad range of illustrations is embedded throughout, including classical and modern results for covariance estimation, clustering, networks, semidefinite programming, coding, dimension reduction, matrix completion, machine learning, compressed sensing, and sparse regression.Product Identifiers
PublisherCambridge University Press
ISBN-139781108415194
eBay Product ID (ePID)5046661720
Product Key Features
Number of Pages296 Pages
LanguageEnglish
Publication NameHigh-Dimensional Probability: an Introduction with Applications in Data Science
Publication Year2018
SubjectEconomics, Computer Science, Mathematics
TypeTextbook
AuthorRoman Vershynin
Subject AreaData Analysis
SeriesCambridge Series in Statistical and Probabilistic Mathematics
FormatHardcover
Dimensions
Item Height260 mm
Item Weight710 g
Item Width183 mm
Additional Product Features
Country/Region of ManufactureUnited Kingdom
Title_AuthorRoman Vershynin
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