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An integrated presentation of theory, applications and algorithms that demonstrates how useful simple stochastic (random) models can be for gaining insight into the behaviour of complex stochastic systems. The methods described can be used to obtain solutions to problems in statistics, operations research, finance, economics and engineering. Following the success of the first edition, this updated edition will include new introductory material, and additional exercises specifically designed to enable the student to understand the solutions and the computations involved. New applications in finance and economics are included, along with discussions of software used for stochastic modeling.
Inhaltsverzeichnis
From the contents:
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.
Appendices.
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.
