Probability
Theory and Examples
4th Edition
Published on 30. September 2010
978-0-511-91090-6 (ISBN)
Description
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.
Reviews / Votes
'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.' Miklos Bona, SIGACT NewsMore details
Series
Edition
4th Revised edition
Language
English
Place of publication
Cambridge
United Kingdom
Publishing group
Cambridge University Press
Target group
Professional and scholarly
Edition type
Revised edition
Illustrations
Worked examples or Exercises; 23 Line drawings, unspecified
File size
3,21 MB
ISBN-13
978-0-511-91090-6 (9780511910906)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
08/2010
4th Edition
Cambridge University Press
€79.22
Article exhausted; check for reprint
Person
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.
Content
1. Measure theory; 2. Laws of large numbers; 3. Central limit theorems; 4. Random walks; 5. Martingales; 6. Markov chains; 7. Ergodic theorems; 8. Brownian motion; Appendix A. Measure theory details.
