An Introduction to Stochastic Modeling, Fifth Edition bridges the gap between basic probability and an intermediate level course in stochastic processes, serving as the foundation for either a one-semester or two-semester course in stochastic processes for students familiar with elementary probability theory and calculus. The objectives are to introduce students to the standard concepts and methods of stochastic modeling, to illustrate the rich diversity of applications of stochastic processes in the applied sciences, and to provide an integrated treatment of theory, applications and practical implementation. A well-regarded resource for many years, the text is an ideal foundation for a broad range of students.
1. Chapter 1: Introduction - p. 1 2. Chapter 2: Conditional probability and conditional expectation - p. 61 3. Chapter 3: Markov chains: introduction - p. 111 4. Chapter 4: The long run behavior of Markov chains - p. 219 5. Chapter 5: Poisson processes - p. 293 6. Chapter 6: Continuous time Markov chains - p. 357 7. Chapter 7: Renewal phenomena - p. 445 8. Chapter 8: Queueing systems - p. 499 9. Chapter 9: Brownian motion and related processes - p. 555 10. Chapter 10: Modeling using stochastic differential equations - p. 599 11. Further reading - p. 661 12. Answers to exercises - p. 663 13. Index - p. 683
An Introduction to Stochastic Modeling, Fifth Edition bridges the gap between basic probability and an intermediate level course in stochastic processes, serving as the foundation for either a one-semester or two-semester course in stochastic processes for students familiar with elementary probability theory and calculus. The objectives are to introduce students to the standard concepts and methods of stochastic modeling, to illustrate the rich diversity of applications of stochastic processes in the applied sciences, and to provide an integrated treatment of theory, applications and practical implementation. A well-regarded resource for many years, the text is an ideal foundation for a broad range of students.
1. Chapter 1: Introduction - p. 1 2. Chapter 2: Conditional probability and conditional expectation - p. 61 3. Chapter 3: Markov chains: introduction - p. 111 4. Chapter 4: The long run behavior of Markov chains - p. 219 5. Chapter 5: Poisson processes - p. 293 6. Chapter 6: Continuous time Markov chains - p. 357 7. Chapter 7: Renewal phenomena - p. 445 8. Chapter 8: Queueing systems - p. 499 9. Chapter 9: Brownian motion and related processes - p. 555 10. Chapter 10: Modeling using stochastic differential equations - p. 599 11. Further reading - p. 661 12. Answers to exercises - p. 663 13. Index - p. 683
