Cambridge Series in Statistical and Probabilistic MathematicsSeries Number 47- High-Dimensional Probability An Introduction with Applications in Data Science

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  • Engels
  • Hardcover
  • 9781108415194
  • 27 september 2018
  • 296 pagina's
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Samenvatting

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.

Productspecificaties

Inhoud

Taal
en
Bindwijze
Hardcover
Oorspronkelijke releasedatum
27 september 2018
Aantal pagina's
296
Illustraties
Nee

Betrokkenen

Hoofdauteur
Roman Vershynin
Hoofduitgeverij
Cambridge University Press

Overige kenmerken

Extra groot lettertype
Nee
Product breedte
183 mm
Product hoogte
22 mm
Product lengte
260 mm
Studieboek
Ja
Verpakking breedte
168 mm
Verpakking hoogte
25 mm
Verpakking lengte
259 mm
Verpakkingsgewicht
905 g

EAN

EAN
9781108415194
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