High-Dimensional Probability: An Introduction with Applications in Data Science (Cambridge Series in Statistical and Probabiistic Mathematics)

Vershynin, Roman:

ISBN 10: 1108415199 / ISBN 13: 9781108415194

Published by Cambridge University Press, 2018

About this Item

300 pp. Very well preserved copy 326 Sprache: Englisch Gewicht in Gramm: 711.

Seller Inventory # 39704

Bibliographic Details

• Title: High-Dimensional Probability: An Introduction with Applications in Data Science (Cambridge Series in Statistical and Probabiistic Mathematics)
• Publisher: Cambridge University Press
• Publication Date: 2018
• Language: English
• ISBN 10: 1108415199
• ISBN 13: 9781108415194
• Binding: Hardcover
• Condition: Sehr gut
• Seller catalogs:
  • Computer

About this title

Synopsis

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.

About the Author

Roman Vershynin is Professor of Mathematics at the University of California, Irvine. He studies random geometric structures across mathematics and data sciences, in particular in random matrix theory, geometric functional analysis, convex and discrete geometry, geometric combinatorics, high-dimensional statistics, information theory, machine learning, signal processing, and numerical analysis. His honors include an Alfred Sloan Research Fellowship in 2005, an invited talk at the International Congress of Mathematicians in Hyderabad in 2010, and a Bessel Research Award from the Humboldt Foundation in 2013. His 'Introduction to the Non-Asymptotic Analysis of Random Matrices' has become a popular educational resource for many new researchers in probability and data science.