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
Inhalt
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.