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Sunday, February 9, 2020 | History

5 edition of High dimensional probability III found in the catalog.

High dimensional probability III

  • 236 Want to read
  • 22 Currently reading

Published by Birkhäuser in Basel, Boston .
Written in English

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

  • Edition Notes

    Includes bibliographical references.

    Other titlesHigh dimensional probability 3.
    StatementJørgen Hoffmann-Jørgensen, Michael B. Marcus, Jon A. Wellner, editors.
    GenreCongresses.
    SeriesProgress in probability -- v. 55., Progress in probability -- 55.
    ContributionsHoffmann-Jørgensen, J. 1929-, Marcus, Michael B., Wellner, Jon A., 1945-
    Classifications
    LC ClassificationsQA273.A1 I554 2002, QA273.A1 I554 2002
    The Physical Object
    Paginationviii, 346 p. :
    Number of Pages346
    ID Numbers
    Open LibraryOL18211046M
    ISBN 103764321873, 0817621873
    LC Control Number2003063669

    Please contact google or the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately. A short version of this book, condensed into just four lectures, can be found here. Free shipping for individuals worldwide Usually dispatched within 3 to 5 business days. I believe, as the author does, that the topics covered in this book are indeed essential ingredients of the developing foundations of data science.

    In part, this was because, by working on a general Banach space, one had to discard the extra, and often extraneous, structure imposed by random variables taking values in a Euclidean space, or by processes being indexed by sets in R or Rd. Many figures look nicer thanks to my student Jennifer Bryson. I am just saying that these books don't fit my particular need. Concentration without independence; 6. As further evidence of this, van Handel devotes more time to lower bounds, and the end of the book is devoted to characteristic exponents and cutoff phenomena, which is strictly a probability theory topic--it amounts to describing beautiful mathematical phenomena in great detail.

    The topics covered here are a must-know for anyone looking to do mathematical work in the field, covering subjects important in machine learning, algorithms and theoretical computer science, signal processing, and applied mathematics. Quadratic forms, symmetrization and contraction; 7. It then introduces the powerful methods based on stochastic processes, including such tools as Slepian's, Sudakov's, and Dudley's inequalities, as well as generic chaining and bounds based on VC dimension. The difficulty of exercises will be indicated by the number of coffee cups one may need to solve them. May 23,


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High dimensional probability III book

Let me qualify what I am looking for. The most remarkable feature of this area is that it has resulted in the creation of powerful new tools and perspectives, whose range of application has led to interactions with other subfields of mathematics, statistics, and computer science.

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Now there are a bunch of books out there with these very words in the title. News February 12, I am not sure if I should be looking at a book on measure theory, or calculus on manifolds, or where? His treatment of the subject is gentle, thorough and inviting, providing a great resource for both newcomers and those familiar with the subject.

The major technical hurdle is due to the possible dependence of the sample under the finite population framework. A basic probability text book will begin with a definition of random variables and work its way towards the Central Limit Theorem.

Basically I am looking for the equivalent of Terry Tao's 2 volume set on Analysis, but for high dimensional probability. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.

Statisticians care chiefly about bounding the probabilities of bad events, and accordingly Vershynin's book does not really cover lower bounds, nor does it cover more delicate concentration phenomena such as hypercontractivity.

Thanks to the feedback of the readers, multiple clarifications and corrections were incorporated. As further evidence of this, van Handel devotes more time to lower bounds, and the end of the book is devoted to characteristic exponents and cutoff phenomena, which is strictly a probability theory topic--it amounts to describing beautiful mathematical phenomena in great detail.

The widely accessible presentation will make this book a classic that everyone in foundational data science will want to have on their bookshelf. I am looking for the same treatment for high dimensional data. These include random matrices, nonparametric statistics, empirical processes, statistical learning theory, concentration of measure phenomena, strong and weak approximations, functional estimation, combinatorial optimization, random graphs, information theory and convex geometry.

However, I do feel confident that the two are different in the way I've described. It is the f Roman Vershynin's wonderful text fills a major gap in the literature by providing a highly accessible introduction to this area. Random matrices; 5. Free shipping for individuals worldwide Usually dispatched within 3 to 5 business days.

It also led to the creation or introduction of powerful new tools, such as randomization, decoupling, moment and exponential inequalities, chaining, isoperimetry and concentration of measure, which apply to areas well beyond those for which they were created.

The topics covered here are a must-know for anyone looking to do mathematical work in the field, covering subjects important in machine learning, algorithms and theoretical computer science, signal processing, and applied mathematics.

To make a gross generalization, the results in van Handel are more often useful to a probabilist who seeks to describe the behavior of a very specific, "nice" random process in as much detail as possible than to a statistician who is primarily interested in proving upper bounds under very general conditions.

This book should be essential reading for students and researchers in probability theory, data science, and related fields.

High Dimensional Probability Iii (Repost)

Please contact google or the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.Introduction to High-Dimensional Statistics is a concise guide to state-of-the-art models, techniques, and approaches for handling high-dimensional data.

The book is intended to expose the reader to the key concepts and ideas in the most simple settings possible while avoiding unnecessary technicalities. High Dimensional Probability III J. Hoffmann-Jørgensen (auth.), Jørgen Hoffmann-Jørgensen, Jon A.

Wellner, Michael B. Marcus (eds.) The title High Dimensional Probability is an attempt to describe the many trib­ utaries of research on Gaussian processes and probability in Banach spaces that started in the early 's.

'High-dimensional probability is a fascinating mathematical theory that has rapidly grown in recent years. It is fundamental to high-dimensional statistics, machine learning and data science.

Progress in Probability: High Dimensional Probability II 47 (2000, Hardcover)

In this book, Roman Vershynin, who is a leading researcher in high-dimensional probability and a Cited by: Oct 23,  · High Dimensional Probability III by Joergen Hoffmann-Joergensen,available at Book Depository with free delivery worldwide.

Feb 21,  · 'This book provides an in-depth mathematical treatment and methodological intuition of high-dimensional statistics. The main technical tools from probability theory are carefully developed and the construction and analysis of statistical methods and algorithms for high-dimensional problems is presented in an outstandingly clear way/5(8).

High Dimensional Probability for Mathematicians and Data Scientists Preface Who is this book for? This is a textbook in probability in high dimen-sions with a view toward applications in data sciences.

It will be useful for doctoral and advanced masters students in mathematics, statistics, electri- Examples of high dimensional.