Understanding Probability is a unique and stimulating approach to a first course in probability. The first part of the book demystifies probability and uses many wonderful probability applications from everyday life to help the reader develop a feel for probabilities. The second part, covering a wide range of topics, teaches clearly and simply the basics of probability. This fully revised third edition has been packed with even more exercises and examples and it includes new sections on Bayesian inference, Markov chain Monte-Carlo simulation, hitting probabilities in random walks and Brownian motion, and a new chapter on continuous-time Markov chains with applications. Here you will find all the material taught in an introductory probability course. The first part of the book, with its easy-going style, can be read by anybody with a reasonable background in high school mathematics. The second part of the book requires a basic course in calculus.
|Publisher:||Cambridge University Press|
|Edition description:||New Edition|
|Product dimensions:||8.70(w) x 5.90(h) x 1.30(d)|
About the Author
Henk Tijms is Emeritus Professor at Vrije University in Amsterdam. He is the author of several textbooks and numerous papers on applied probability and stochastic optimization. In 2008 Henk Tijms received the prestigious INFORMS Expository Writing Award for his publications and books.
Table of Contents
Preface; Introduction; Part I. Probability in Action: 1. Probability questions; 2. The law of large numbers and simulation; 3. Probabilities in everyday life; 4. Rare events and lotteries; 5. Probability and statistics; 6. Chance trees and Bayes' rule; Part II. Essentials of Probability: 7. Foundations of probability theory; 8. Conditional probability and Bayes; 9. Basic rules for discrete random variables; 10. Continuous random variables; 11. Jointly distributed random variables; 12. Multivariate normal distribution; 13. Conditioning by random variables; 14. Generating functions; 15. Discrete-time Markov chains; 16. Continuous-time Markov chains; Appendix; Counting methods and ex; Recommended reading; Answers to odd-numbered problems; Bibliography; Index.