9781108415194-1108415199-High-Dimensional Probability: An Introduction with Applications in Data Science (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 47)

High-Dimensional Probability: An Introduction with Applications in Data Science (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 47)

ISBN-13: 9781108415194
ISBN-10: 1108415199
Edition: 1
Author: Roman Vershynin
Publication date: 2018
Publisher: Cambridge University Press
Format: Hardcover 296 pages
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ISBN-13: 9781108415194
ISBN-10: 1108415199
Edition: 1
Author: Roman Vershynin
Publication date: 2018
Publisher: Cambridge University Press
Format: Hardcover 296 pages

Summary

High-Dimensional Probability: An Introduction with Applications in Data Science (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 47) (ISBN-13: 9781108415194 and ISBN-10: 1108415199), written by authors Roman Vershynin, was published by Cambridge University Press in 2018. With an overall rating of 4.3 stars, it's a notable title among other Applied (Mathematics) books. You can easily purchase or rent High-Dimensional Probability: An Introduction with Applications in Data Science (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 47) (Hardcover) from BooksRun, along with many other new and used Applied books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $36.26.

Description

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.

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