High-Dimensional Probability 2025 pdf epub mobi 电子书

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High-Dimensional Probability 2025 pdf epub mobi 电子书著者简介

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.

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发表于2025-03-26

High-Dimensional Probability 2025 pdf epub mobi 电子书

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这个书居然作者不给errata也是很不user-friendly了. 根据已经出版的版本, 发现的数学错误或typo错误如下: p58, Exercise 3.5.3: 命题不总成立. 可补充条件"A对角线为0或A是PSD". Online version该处已修正. p83, Exercise 4.3.7(b): t log_2 (e/t) 疑应为 t log_2 (1/t). p96, ...

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出版者:Cambridge University Press

作者:Roman Vershynin

出品人:

页数:296

译者:

出版时间:2018-9-27

价格:USD 69.99

装帧:Hardcover

isbn号码:9781108415194

丛书系列:Cambridge Series in Statistical and Probabilistic Mathematics

图书标签: 数学 Statistics 统计学概率论 Machine_Learning DataScience 统计 statistics

High-Dimensional Probability 2025 pdf epub mobi 电子书图书描述

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.

High-Dimensional Probability 2025 pdf epub mobi 电子书

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