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Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024

Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics)
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R. M. Dudley 作者
Cambridge University Press
譯者
2008-02-04 出版日期
452 頁數
USD 50.00 價格
Paperback
Cambridge Studies in Advanced Mathematics 叢書系列
9780521052214 圖書編碼

Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics) 在線電子書 圖書標籤: Statistics  Mathematics   

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Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics) 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics) 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


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Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics) 在線電子書 著者簡介

Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics) 在線電子書 著者簡介

Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics) 在線電子書 圖書描述

This book shows how the central limit theorem for independent, identically distributed random variables with values in general, multidimensional spaces, holds uniformly over some large classes of functions. The author, an acknowledged expert, gives a thorough treatment of the subject, including several topics not found in any previous book, such as the Fernique-Talagrand majorizing measure theorem for Gaussian processes, an extended treatment of Vapnik-Chervonenkis combinatorics, the Ossiander L2 bracketing central limit theorem, the Gine-Zinn bootstrap central limit theorem in probability, the Bronstein theorem on approximation of convex sets, and the Shor theorem on rates of convergence over lower layers. Other results of Talagrand and others are surveyed without proofs in separate sections. Problems are included at the end of each chapter so the book can be used as an advanced text. The book will interest mathematicians working in probability, mathematical statisticians and computer scientists working in computer learning theory.

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