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- Product code: 14341
- ISBN: 0471498807,
ISBN13: 9780471498803,
450 pages, hardback
Published by John Wiley & Sons on 2000
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Description of A First Course in Stochastic Models |
The field of applied probability has changed profoundly in the past twenty years. The development of computational methods has greatly contributed to a better understanding of the theory. A First Course in Stochastic Models provides a self-contained introduction to the theory and applications of stochastic models.
Emphasis is placed on establishing the theoretical foundations of the subject, thereby providing a framework in which the applications can be understood. Without this solid basis in theory no applications can be solved.
- 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.
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Contents of A First Course in Stochastic Models |
Preface
The Poisson Process and Related Processes
Renewal-Reward 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
References
Index
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