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.
"synopsis" may belong to another edition of this title.
'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.
"About this title" may belong to another edition of this title.
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