File:Monte-carlo-and-stochastic-simulation-methods fig1.png

The Monte Carlo approach for transferring input uncertainty into a distribution of response values. (a) Ideal case—The input space and transfer function are perfectly known, resulting in the exact response values. (b) Traditional approach—The input values are interpolated from sparse data and the actual transfer function is estimated, resulting in estimated response values with usually no assessment of their uncertainty. (c) Monte Carlo approach—Input uncertainty is modeled by a series of equiprobable input sets which, after processing, provide a probability distribution (pdf) for the response value(s).