圖書標籤: 數學 Statistics 統計學 概率論 Machine_Learning DataScience 統計 statistics
发表于2024-12-28
High-Dimensional Probability pdf epub mobi txt 電子書 下載 2024
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.
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.
這個寫得淺顯易懂, 為瞭讓更多做應用的人讀懂吧. 第1-6章寫得很好, 後麵就有點意猶未盡草草收尾(特彆是第11章). 其實應該多寫一點幾何泛函分析的, 比如john's ellipsoid什麼的. Roman Vershynin其實是從幾何泛函分析(他的專長)看這些問題, 與之對應的是van handel的notes, 就更加是從概率論的概率論看這些問題(所以有更多的篇幅討論更精細的lower bound, 還有hypercontractivity這樣的內容). 但是幾何泛函分析一般不看hypercontractivity(畢竟主要是boolean cube上的), 即便是ledoux和talagrand的書也不提這個.
評分這個寫得淺顯易懂, 為瞭讓更多做應用的人讀懂吧. 第1-6章寫得很好, 後麵就有點意猶未盡草草收尾(特彆是第11章). 其實應該多寫一點幾何泛函分析的, 比如john's ellipsoid什麼的. Roman Vershynin其實是從幾何泛函分析(他的專長)看這些問題, 與之對應的是van handel的notes, 就更加是從概率論的概率論看這些問題(所以有更多的篇幅討論更精細的lower bound, 還有hypercontractivity這樣的內容). 但是幾何泛函分析一般不看hypercontractivity(畢竟主要是boolean cube上的), 即便是ledoux和talagrand的書也不提這個.
評分19年剛齣的書,對工科背景的學生學習理論工具和數學係學生瞭解應用背景都有參考價值。想要更嚴謹地瞭解這些理論可以去讀Handel的notes,還沒齣版但是掛在Princeton他的個人主頁上。
評分19年剛齣的書,對工科背景的學生學習理論工具和數學係學生瞭解應用背景都有參考價值。想要更嚴謹地瞭解這些理論可以去讀Handel的notes,還沒齣版但是掛在Princeton他的個人主頁上。
評分寫得很棒!
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, ...
評分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, ...
評分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, ...
評分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, ...
評分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, ...
High-Dimensional Probability pdf epub mobi txt 電子書 下載 2024