Probability: Theory and Examples (Google eBook)

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Cambridge University Press, Aug 30, 2010 - Mathematics - 428 pages
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This classic introduction to probability theory for beginning graduate students covers 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. The new edition begins with a short chapter on measure theory to orient readers new to the subject.
  

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Contents

1 Measure Theory
1
2 Laws of Large Numbers
41
3 Central Limit Theorems
94
4 Random Walks
179
5 Martingales
221
6 Markov Chains
274
7 Ergodic Theorems
328
8 Brownian Motion
353
Measure Theory Details
401
References
419
Index
425
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About the author (2010)

Rick Durrett received his Ph.D. in Operations Research from Stanford University in 1976. After nine years at UCLA and twenty-five 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.

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