This text is designed for a one-term or one-year course in probability. Throughout the book, as theory is developed, Durrett presents many examples and emphasizes results that are useful in solving problems. An appendix gives complete proofs of the results needed to form measure theory. Also included are a numer of nonstandard topics, such as large deviations, local limit theoremcs, renewal theory, Markov chains on general state space, subadditive ergodic theory, and central limit theorems for stationary sequences and martingales. This text is also a useful reference for people who study probability or apply it in their work. This book should be of interest to first year undergraduates with experience of calculus taking courses in probability.
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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. 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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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