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Link Either by signing into your account or linking your membership details before your order is placed. Description Table of Contents Product Details Click on the cover image above to read some pages of this book! Industry Reviews 'This is an excellent and very timely text, presenting the modern tools of high-dimensional geometry and probability in a very accessible and applications-oriented manner, with plenty of informative exercises.

- Cambridge Series in Statistical and Probabilistic Mathematics: Probability. Theory and Examples.
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Preface; Appetizer: using probability to cover a geometric set; 1. Preliminaries on random variables; 2. Concentration of sums of independent random variables; 3. Random vectors in high dimensions; 4. Random matrices; 5. Concentration without independence; 6. Quadratic forms, symmetrization and contraction; 7.

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Random processes; 8. Chaining; 9. Deviations of random matrices and geometric consequences; Sparse recovery; Dvoretzky-Milman's theorem; Bibliography; Index. In Stock.

## Cambridge Statistics Books - PDF Drive

The Art of Statistics Learning from Data. Statistics for The Behavioral Sciences 10th Edition. Stats Data and Models, Global Edition. Statistics - 4th Edition International Student Edition. Beyond Coincidence.

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## High-Dimensional Probability

Remove From Wishlist Cancel. Shipped from UK. Established seller since Seller Inventory IQ Delivered from our UK warehouse in 4 to 14 business days. Paperback or Softback. Seller Inventory BBS Book Description Condition: New. Seller Inventory n. Book Description Cambridge Univ Pr, Condition: Brand New. In Stock. Never used! This item is printed on demand. Seller Inventory Seller Inventory LIE David Pollard.

## Cambridge Series in Statistical and Probabilistic Mathematics: Probability: Theory and Examples

Publisher: Cambridge University Press , This specific ISBN edition is currently not available. View all copies of this ISBN edition:. Synopsis About this title This book grew from a one-semester course offered for many years to a mixed audience of graduate and undergraduate students who have not had the luxury of taking a course in measure theory. Book Description : Rigorous probabilistic arguments, built on the foundation of measure theory introduced seventy years ago by Kolmogorov, have invaded many fields.

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