Modern and measure-theory based, this text is intended primarily for the first-year graduate course in probability theory.
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'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.' Miklós Bóna, SIGACT News
This book is an 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.
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