By
Gerald Shedler, IBM Research Divison
Description
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.
Audience:
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.
