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Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics) 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics)
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简体网页||繁体网页
R. M. Dudley 作者
Cambridge University Press
译者
2008-02-04 出版日期
452 页数
USD 50.00 价格
Paperback
Cambridge Studies in Advanced Mathematics 丛书系列
9780521052214 图书编码

Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics) 在线电子书 图书标签: Statistics  Mathematics   

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发表于2024-11-07

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Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics) 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics) 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics) 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


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Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics) 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics) 在线电子书 图书描述

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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