High-Dimensional Probability: An Introduction with

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

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.
Country USA
Brand Cambridge University Press
Manufacturer Cambridge University Press
Binding Hardcover
ItemPartNumber 32549805
ReleaseDate 2018-09-27
UnitCount 1
EANs 9781108415194

Country	USA
Brand	Cambridge University Press
Manufacturer	Cambridge University Press
Binding	Hardcover
ItemPartNumber	32549805
ReleaseDate	2018-09-27
UnitCount	1
EANs	9781108415194

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

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