Concentration inequalities for functions of independent random variables is an area of probability t
| 商家名称 |
信用等级 |
购买信息 |
订购本书 |
|
|
 |
Concentration Inequalities: A Nonasymptotic Theory of Independence |
 |
|
|
Concentration Inequalities: A Nonasymptotic Theory of Independence |
|
Concentration inequalities for functions of independent random variables is an area of probability theory that has witnessed a great revolution in the last few decades, and has applications in a wide variety of areas such as machine learning, statistics, discrete mathematics, and high-dimensional geometry. Roughly speaking, if a function of many independent random variables does not depend too much on any of the variables then it is concentrated in the sense that with high probability, it is close to its expected value. This book offers a host of inequalities to illustrate this rich theory in an accessible way by covering the key developments and applications in the field.
The authors describe the interplay between the probabilistic structure (independence) and a variety of tools ranging from functional inequalities to transportation arguments to information theory. Applications to the study of empirical processes, random projections, random matrix theory, and threshold phenomena are also presented.
A self-contained introduction to concentration inequalities, it includes a survey of concentration of sums of independent random variables, variance bounds, the entropy method, and the transportation method. Deep connections with isoperimetric problems are revealed whilst special attention is paid to applications to the supremum of empirical processes.
Written by leading experts in the field and containing extensive exercise sections this book will be an invaluable resource for researchers and graduate students in mathematics, theoretical computer science, and engineering.
网友对Concentration Inequalities: A Nonasymptotic Theory of Independence的评论
Dry math. Not to learn about the subject.
This book is a gem. In its breadth of coverage, in providing context
and intuition, in writing accessible proofs, in explaining room for
improvement, in supplying charming exercises, and in delivering the joy of the unique subject called 'mathematics', it stands head and shoulders
above most books I have ever read.
This book will be definitely interesting for anyone in computer science, dealing with probability and statistics. It covers a huge work in the area of concentration inequalities and describes in a friendly way most of the results, which were previously scattered among different papers. Very useful! And nicely written!
Concentration inequalities are a powerful tool in many machine learning topics.
I strongly recommend this book to anyone who wants to do some research on machine learning.
This is a truly wonderful book, that manages to convey the richness of concentration inequalities while maintaining
clarity and focus. Connections to entropy, influences, convex geometry and isoperimetric inequalities are highlighted.
The book should be useful for novices as well as seasoned experts. A remarkable achievement.
喜欢Concentration Inequalities: A Nonasymptotic Theory of Independence请与您的朋友分享,由于版权原因，读书人网不提供图书下载服务
