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A First Course in Stochastic Models By: Henk Tijms Normal price: 90 Our price: 76.5 + postage Bargain: save £13.5 ! Product code: 14341 ISBN: 0471498807 450 pages Format: Hb Published by: John Wiley & Sons, 2000, 2nd edition There are no reviews available for this book 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. A First Course in Stochastic Models - Chapter headings 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