Chapter 7 Implementing Representations of Uncertainty

Abstract Chapters 3–6 discussed generating the basic random numbers that drive a stochastic simulation, as well as algorithms to convert them into realizations of random input structures like random variables, random vectors, and stochastic processes. Still, there could be different ways to implement such methods and algorithms in a given simulation model, and the choices made can affect the nature and quality of the output. Of particular interest is precisely how the basic random numbers being generated might be used to generate the random input structures for the model. This chapter discusses these and related issues and suggests how implementation might best proceed, not only in particular simulation models but also in simulation software.