MATH/STAT 235A Probability Theory Lecture Notes. Theory and Examples, 4th Ed. By Rick Durrett. References to the earlier 3rd edition appear as.

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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 fourth edition begins with a short chapter on measure theory to orient readers new to the subject. Rick Durrett received his PhD 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 eight books and more than 170 journal articles on a wide variety of topics, and he has supervised more than 40 PhD students.

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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.