Univariate input models for stochastic simulation

Techniques are presented for modelling and then randomly sampling many of the continuous univariate probabilistic input processes that drive discrete-event simulation experiments. Emphasis is given to the generalized beta distribution family, the Johnson translation system of distributions, and the Bézier distribution family because of the flexibility of these families to model a wide range of distributional shapes that arise in practical applications. Methods are described for rapidly fitting these distributions to data or to subjective information (expert opinion) and for randomly sampling from the fitted distributions. Also discussed are applications ranging from pharmaceutical manufacturing and medical decision analysis to smart-materials research and health-care systems analysis.