Theory and Examples
- Author: Rick Durrett, Duke University, North Carolina
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.Read more
- More than 250 good examples and 500 exercises
- Comprehensive treatment in only 400 pages
- Concentrates on results useful for application
Reviews & endorsements
'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
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- Edition: 4th Edition
- Date Published: September 2010
- format: Adobe eBook Reader
- isbn: 9780511910906
- contains: 23 b/w illus. 532 exercises
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
1. Measure theory
2. Laws of large numbers
3. Central limit theorems
4. Random walks
6. Markov chains
7. Ergodic theorems
8. Brownian motion
Appendix A. Measure theory details.
Instructors have used or reviewed this title for the following courses
- Graduate Probability
- Probability Math
- Quantitative analaysis
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