ABSTRACT
A dynamic system can be viewed as either a sequence of random variables or a stochastic process. Random variables are mathematical abstractions which are defined on the sample space of a random experiment. This chapter discusses random variables and stochastic processes in the context of discrete-event simulation. A continuous random variable has a special function referred to as the Probability Density Function (PDF). A Bernoulli random variable is a discrete random variable that can be used for modeling random experiments with two outcomes only. The random experiment of repeating a Bernoulli trial until the first success is observed is modeled by a geometric random variable. The Poisson random variable is used to represent the number of events that occur in an interval of time of fixed duration throughout the random experiment. The events can be modeled as equally likely because the PDF is constant for all the possible values of the random variable.
