A First Course in Stochastic Models
- Provides an introduction to the use of stochastic models through an integrated presentation of theory, algorithms and applications.
- Incorporates recent developments in computational probability.
- Includes a wide range of examples that illustrate the models and make the methods of solution clear.
- Features an abundance of motivating exercises that help the student learn how to apply the theory.
- Accessible to anyone with a basic knowledge of probability.
A First Course in Stochastic Models is suitable for senior undergraduate and graduate students from computer science, engineering, statistics, operations resear ch, and any other discipline where stochastic modelling takes place. It stands out amongst other textbooks on the subject because of its integrated presentation of theory, algorithms and applications.
The Poisson Process and Related Processes.
Discrete-Time Markov Chains.
Continuous-Time Markov Chains.
Markov Chains and Queues.
Discrete-Time Markov Decision Processes.
Semi-Markov Decision Processes.
Advanced Renewal Theory.
Algorithmic Analysis of Queueing Models.
Appendix A: Useful Tools in Applied Probability.
Appendix B: Useful Probability Distributions.
Appendix C: Generating Functions.
Appendix D: The Discrete Fast Fourier Transform.
Appendix E: Laplace Transform Theory.
Appendix F: Numerical Laplace Inversion.
Appendix G: The Root-Finding Problem.
- Fully updated with enhanced introductory material
- Presents an integrated presentation of theory, applications and algorithms
- Incorporates recent developments in computational probability
- Includes a wide range of real-world examples that illustrate the basic models and elucidate the methods of solution
- Accessible to anyone with knowledge of calculus and probability