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Tuesday, July 21, 2020 | History

1 edition of High dimensional probability II found in the catalog.

High dimensional probability II

by Evarist Gine

  • 287 Want to read
  • 29 Currently reading

Published by Springer Science+Business Media in New York .
Written in English

    Subjects:
  • Probabilities,
  • Gaussian processes,
  • Linear topological spaces

  • Edition Notes

    Includes bibliographical references.

    StatementEvarist Gine, David M. Mason, Jon A. Wellner, editors
    SeriesProgress in probability -- v 47
    ContributionsWellnerr, Jon A., 1945-, e-libro, Corp
    Classifications
    LC ClassificationsQA273 .H6435 2000eb
    The Physical Object
    Format[recurso electronico] /
    Pagination1 online resource (x, 510 p.)
    Number of Pages510
    ID Numbers
    Open LibraryOL27043410M
    ISBN 109781461271116, 9781461213581
    OCLC/WorldCa849951101

    In Parts I and II, all results are proved, making this the first self-contained text discussing high-dime. nsional percolation. Part III, consisting of Chapters 10–13, describes recent progress in high-dimensional percolation. Partial proofs and substantial overviews of how the proofs are obtained are given. The term High Dimensional Probability, and Probability in Banach spaces be-fore, refersto researchin probability and statisticsthat emanated fromthe problems mentioned above and the developments that resulted from such studies. A large portion of the material presented here is centered on these topics. For.

    In a basic course in probability theory, we learned about the two most im-portant quantities associated with a random variable X, namely the expec-tation1 (also called mean), and variance. They will be denoted in this book by EX and Var(X) = E(X EX)2: Let us recall some other classical quantities and functions that describe probability File Size: 2MB.   High Dimensional Probability III by Joergen Hoffmann-Joergensen, , available at Book Depository with free delivery worldwide.

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High dimensional probability II by Evarist Gine Download PDF EPUB FB2

I am Professor of Mathematics at the University of California, Irvine working in high-dimensional probability theory and its applications. I study probabilistic structures that appear across mathematics and data sciences, in particular random matrix theory, geometric functional analysis, convex and discrete geometry, high-dimensional statistics, information theory, learning theory, signal.

This book covers only a fraction of theoretical apparatus of high-dimensional probability, and it illustrates it with only a sample of data science applications. Each chapter in this book is concluded with a Notes section, which has pointers to other texts on the matter.

High dimensional probability, in the sense that encompasses the topics rep­ resented in this volume, began about thirty years ago with research in two related areas: limit theorems for sums of independent Banach space valued random vectors and general Gaussian processes.

ISBN: OCLC High dimensional probability II book Description: x, pages ; 25 cm. Contents: Moment Bounds for Self-Normalized Martingales / Victor H. de la Pena, Michael J.

Klass and Tze Leung Lai --Exponential and Moment Inequalities for U-Statistics / Evarist Gine, Rafal Latala and Joel Zinn --A Multiplicative Inequality for Concentration Functions of n. : High Dimensional Probability Ii (Progress in Probability) (): Giné, Evarist,David, r, Jon: Books.

High dimensional probability, in the sense that encompasses the topics rep- resented in this volume, began about thirty years ago with research in two related areas: limit theorems for sums of independent Banach space valued random vectors and general Gaussian processes.

In this book, Roman Vershynin, who is a leading researcher in high-dimensional probability and a master of exposition, provides the basic tools and some of the main results and applications of high-dimensional probability. This book is an excellent textbook for a graduate course that will be appreciated by mathematics, statistics, computer Cited by: High Dimensional Probability II.

a new algorithm for this problem is proposed, and high probability bounds on its simple and cumulative regret are established. (Eds.), High Dimensional.

1 I highly recommend the book in progress (as of ) by Roman Vershynin [] for a wonderful introduction to high-dimensional probability and its applications from a very di erent perspective than the one taken in these Size: 1MB. Find many great new & used options and get the best deals for Progress in Probability: High Dimensional Probability II 47 (, Hardcover) at the best online prices at eBay.

Free shipping for many products. Get this from a library. High dimensional probability II. [Evarist Giné; David M Mason; Jon A Wellner;] -- High dimensional probability, in the sense that encompasses the topics rep­ resented in this volume, began about thirty years ago with research in two related areas: limit theorems for sums of.

The book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation.

This is a collection of papers by participants at High Dimensional Probability VI Meeting held from Octoberat the Banff International Research Station in Banff, Alberta, Canada. High Dimensional Probability (HDP) is an area of mathematics that includes the study of probability distributions and limit theorems in infinite-dimensional Format: Hardcover.

High Dimensional Probability II. Our high-probability statements for the unbounded case are based on the small-ball analysis of \cite{Mendelson14}. The first part of the book presents a. Notation Functions, sets, vectors [n] Set of integers [n] = f1;;ng Sd 1 Unit sphere in dimension d 1I() Indicator function jxj q ‘ q norm of xde ned by jxj q= P i jx ij q 1 q for q>0 jxj 0 ‘ 0 norm of xde ned to be the number of nonzero coordinates of x f(k) k-th derivative of f e j j-th vector of the canonical basis Ac complement of set A conv(S) Convex hull of set Size: 1MB.

You can write a book review and share your experiences. Other readers will always be interested in your opinion of the books you've read. Whether you've loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them.

Philippe Rigollet works at the intersection of statistics, machine learning, and optimization, focusing primarily on the design and analysis of statistical methods for high-dimensional problems. His recent research focuses on statistical optimal transport. The book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data analysis.

1. In high dimensions, (nearly) every point is an outlier. Call the dimension of the space p. As p increases, the volume of a unit hypercube increases much faster than the volume of a unit hypersphere.

The number of parameters increases r. This volume collects selected papers from the 7th High Dimensional Probability meeting held at the Institut d'Études Scientifiques de Cargèse (IESC) in Corsica, France. High Dimensional Probability (HDP) is an area of mathematics that includes the study of probability distributions and limit theorems in infinite-dimensional spaces such as.

In a basic course in probability theory, we learned about the two most im-portant quantities associated with a random variable X, namely the expec-tation1 (also called mean), and variance.

They will be denoted in this book by EX and Var(X) = E(X EX)2: Let us recall some other classical quantities and functions that describe probability File Size: 1MB.High Dimensional Probability II的话题 (全部 条) 什么是话题 无论是一部作品、一个人,还是一件事,都往往可以衍生出许多不同的话题。.The feat she has accomplished successfully for this difficult area of statistics is something very few could accomplish.

The wealth of information is enormous and a motivated student can learn a great deal from this book I highly recommend [it] to researchers working in the field of high dimensional data and to motivated graduate students.'Author: Inge Koch.