Resampling methods for input modeling

Stochastic simulation models are used to predict the behavior of real systems whose components have random variation. The simulation model generates artificial random quantities based on the nature of the random variation in the real system. Very often, the probability distributions occurring in the real system are unknown, and must be estimated using finite samples. This paper shows three methods for incorporating the error due to input distributions that are based on finite samples, when calculating confidence intervals for output parameters.