Concentration Inequalities

Concentration Inequalities PDF Author: Stéphane Boucheron
Publisher: Oxford University Press
ISBN: 0199535256
Category : Mathematics
Languages : en
Pages : 492

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Book Description
Describes 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.

Concentration Inequalities

Concentration Inequalities PDF Author: Stéphane Boucheron
Publisher: Oxford University Press
ISBN: 0199535256
Category : Mathematics
Languages : en
Pages : 492

Get Book Here

Book Description
Describes 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.

Concentration Inequalities for Sums and Martingales

Concentration Inequalities for Sums and Martingales PDF Author: Bernard Bercu
Publisher: Springer
ISBN: 3319220993
Category : Mathematics
Languages : en
Pages : 131

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Book Description
The purpose of this book is to provide an overview of historical and recent results on concentration inequalities for sums of independent random variables and for martingales. The first chapter is devoted to classical asymptotic results in probability such as the strong law of large numbers and the central limit theorem. Our goal is to show that it is really interesting to make use of concentration inequalities for sums and martingales. The second chapter deals with classical concentration inequalities for sums of independent random variables such as the famous Hoeffding, Bennett, Bernstein and Talagrand inequalities. Further results and improvements are also provided such as the missing factors in those inequalities. The third chapter concerns concentration inequalities for martingales such as Azuma-Hoeffding, Freedman and De la Pena inequalities. Several extensions are also provided. The fourth chapter is devoted to applications of concentration inequalities in probability and statistics.

Concentration of Measure Inequalities in Information Theory, Communications, and Coding

Concentration of Measure Inequalities in Information Theory, Communications, and Coding PDF Author: Maxim Raginsky
Publisher:
ISBN: 9781601989062
Category : Computers
Languages : en
Pages : 256

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Book Description
Concentration of Measure Inequalities in Information Theory, Communications, and Coding focuses on some of the key modern mathematical tools that are used for the derivation of concentration inequalities, on their links to information theory, and on their various applications to communications and coding.

Concentration Inequalities and Model Selection

Concentration Inequalities and Model Selection PDF Author: Pascal Massart
Publisher: Springer
ISBN: 3540485031
Category : Mathematics
Languages : en
Pages : 346

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Book Description
Concentration inequalities have been recognized as fundamental tools in several domains such as geometry of Banach spaces or random combinatorics. They also turn to be essential tools to develop a non asymptotic theory in statistics. This volume provides an overview of a non asymptotic theory for model selection. It also discusses some selected applications to variable selection, change points detection and statistical learning.

Concentration Inequalities

Concentration Inequalities PDF Author: Stéphane Boucheron
Publisher: OUP Oxford
ISBN: 0191655511
Category : Mathematics
Languages : en
Pages : 492

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Book Description
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.

An Introduction to Matrix Concentration Inequalities

An Introduction to Matrix Concentration Inequalities PDF Author: Joel Aaron Tropp
Publisher:
ISBN: 9781601988393
Category : Matrix derivatives
Languages : en
Pages : 230

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Book Description
Random matrices now play a role in many areas of theoretical, applied, and computational mathematics. Therefore, it is desirable to have tools for studying random matrices that are flexible, easy to use, and powerful. Over the last fifteen years, researchers have developed a remarkable family of results, called matrix concentration inequalities, that achieve all of these goals. This monograph offers an invitation to the field of matrix concentration inequalities. It begins with some history of random matrix theory; it describes a flexible model for random matrices that is suitable for many problems; and it discusses the most important matrix concentration results. To demonstrate the value of these techniques, the presentation includes examples drawn from statistics, machine learning, optimization, combinatorics, algorithms, scientific computing, and beyond.

The Concentration of Measure Phenomenon

The Concentration of Measure Phenomenon PDF Author: Michel Ledoux
Publisher: American Mathematical Soc.
ISBN: 0821837923
Category : Mathematics
Languages : en
Pages : 194

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Book Description
The observation of the concentration of measure phenomenon is inspired by isoperimetric inequalities. This book offers the basic techniques and examples of the concentration of measure phenomenon. It presents concentration functions and inequalities, isoperimetric and functional examples, spectrum and topological applications and product measures.

Stochastic Inequalities and Applications

Stochastic Inequalities and Applications PDF Author: Evariste Giné
Publisher: Birkhäuser
ISBN: 3034880693
Category : Mathematics
Languages : en
Pages : 362

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Book Description
Concentration inequalities, which express the fact that certain complicated random variables are almost constant, have proven of utmost importance in many areas of probability and statistics. This volume contains refined versions of these inequalities, and their relationship to many applications particularly in stochastic analysis. The broad range and the high quality of the contributions make this book highly attractive for graduates, postgraduates and researchers in the above areas.

High-Dimensional Probability

High-Dimensional Probability PDF Author: Roman Vershynin
Publisher: Cambridge University Press
ISBN: 1108415199
Category : Business & Economics
Languages : en
Pages : 299

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Book Description
An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.

Concentration of Measure for the Analysis of Randomized Algorithms

Concentration of Measure for the Analysis of Randomized Algorithms PDF Author: Devdatt P. Dubhashi
Publisher: Cambridge University Press
ISBN: 1139480995
Category : Computers
Languages : en
Pages : 213

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Book Description
Randomized algorithms have become a central part of the algorithms curriculum, based on their increasingly widespread use in modern applications. This book presents a coherent and unified treatment of probabilistic techniques for obtaining high probability estimates on the performance of randomized algorithms. It covers the basic toolkit from the Chernoff–Hoeffding bounds to more sophisticated techniques like martingales and isoperimetric inequalities, as well as some recent developments like Talagrand's inequality, transportation cost inequalities and log-Sobolev inequalities. Along the way, variations on the basic theme are examined, such as Chernoff–Hoeffding bounds in dependent settings. The authors emphasise comparative study of the different methods, highlighting respective strengths and weaknesses in concrete example applications. The exposition is tailored to discrete settings sufficient for the analysis of algorithms, avoiding unnecessary measure-theoretic details, thus making the book accessible to computer scientists as well as probabilists and discrete mathematicians.