Perfect for introductory probability courses and for self-study, this book demystifies much of probability theory, including betting systems and the central limit theorem. This third edition contains even more exercises and examples, plus new sections on Bayesian inference, Markov chain Monte-Carlo simulation, hitting probabilities in random walks and Brownian motion.
Perfect for introductory probability courses and for self-study, this book demystifies much of probability theory, including betting systems and the central limit theorem. This third edition contains even more exercises and examples, plus new sections on Bayesian inference, Markov chain Monte-Carlo simulation, hitting probabilities in random walks and Brownian motion.
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.
Inhaltsangabe
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.
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.
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