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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: 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
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High-Dimensional Probability: An Introduction with Applications in Data Science (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 47) Hardback - 2018

by Vershynin, Roman

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Cambridge University Press. Used - Good. . . All orders guaranteed and ship within 24 hours. Your purchase supports More Than Words, a nonprofit job training program for youth, empowering youth to take charge of their lives by taking charge of a business.
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More Than Words empowers youth who are in foster care, court-involved, homeless or out of school to take charge of their lives by taking charge of a business. MTW believes that when system-involved youth are challenged with authentic and increasing responsibilities in a business setting, and are given high expectations and a culture of support, they can and will address personal barriers to success, create concrete action plans for their lives, and become contributing members of society. More Than Words began as an online bookselling training program for youth in DCF custody in 2004 and opened its vibrant bookstore on Moody St in Waltham in 2005 and added its Starbucks coffee bar in 2008. MTW replicated its model in the South End of Boston in 2011, thereby doubling the number of youth served annually.

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From the publisher

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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