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 Title: Probability: Theory and Examples
 Author(s) Rick Durrett
 Publisher: Cambridge University Press; 5th Ed. (May 30, 2019); eBook (Draft, Version 5, January 11, 2019)
 Permission: The PDF Draft is post by the author.
 Hardcover 430 pages
 eBook PDF (490 pages)
 Language: English
 ISBN10/ASIN: 1108473687
 ISBN13: 9781108473682
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Book Description
This book is a classic introduction to probability theory covering laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. It is a comprehensive treatment concentrating on the results that are the most useful for applications. Its philosophy is that the best way to learn probability is to see it in action, so there are 200 examples and 450 problems.
This book is also an excellent resource. Several interesting and concrete examples are presented throughout the textbook, which will help novices obtain a better understanding of the fundamentals of probability theory
The best feature of the book is its selection of examples. The author has done an extraordinary job in showing not simply what the presented theorems can be used for, but also what they cannot be used for.
The new edition of this lively but rigorous introduction to measure theoretic probability theory, designed for use in a graduate course, contains a new chapter on multidimensional Brownian motion and its relationship to partial differential equations (PDEs), a topic that is finding new applications.
About the Authors Rick Durrett received his Ph.D. in Operations Research from Stanford University in 1976. After nine years at UCLA and twentyfive at Cornell University, he moved to Duke University in 2010, where he is a Professor of Mathematics. He is the author of 8 books and more than 170 journal articles on a wide variety of topics, and he has supervised more than 40 Ph.D. students. He is a member of the National Academy of Science and the American Academy of Arts and Sciences and a Fellow of the Institute of Mathematical Statistics.
 Probability, Stochastic Process, Queueing Theory, etc.
 Statistics and SAS Programming
 Financial Mathematics and Engineering
 Computational and Algorithmic Mathematics
 Combinatorics and Game Theory

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