Simulation is a controlled statistical sampling technique that can be used to study complex stochastic systems when analytic and/or numerical techniques do not suffice. The focus of this book is on simulations of discrete-event stochastic systems; namely, simulations in which stochastic state transitions occur only at an increasing sequence of random times. The discussion emphasizes simulations on a finite or countably infinite state space.
- Develops probabilistic methods for simulation of discrete-event stochastic systems
- Emphasizes stochastic modeling and estimation procedures based on limit theorems for regenerative stochastic processes
- Includes engineering applications of discrete-even simulation to computer, communication, manufacturing, and transportation systems
- Focuses on simulations with an underlying stochastic process that can specified as a generalized semi-Markov process
- Unique approach to simulation, with heavy emphasis on stochastic modeling
- Includes engineering applications for computer, communication, manufacturing, and transportation systems
The presentation is self contained. Some knowledge of elementary probability theory, statistics, and stochastic models is necessary for an understanding of the theory and the examples. Many of the arguments use results often contained in a first year graduate course on stochastic process. A brief review of the necessary material is in Appendix A.
Preface. Discrete-Event Simulations. Regenerative Stochastic Processes. Regenerative Simulation. Networks of Queues. Passage Times. Simulations With Simultaneous Events. Appendix A. Limit Theorems for Stochastic Processes. Appendix B. Random Number Generation.
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- © Academic Press 1993
- 1st October 1992
- Academic Press
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IBM Research Divison