Nominal run and uncertainty analysis

The toolbox distinguishes between two types of simulation runs; the nominal run and the uncertainty analysis loop. The nominal run represents a single simulation run in which the aim is to compute the most likely, unbiased estimates for the model at hands. E.g., when computing dietary exposure distributions, the nominal run computea single exposure distribution, using nominal values for all uncertain values. In the uncertainty analysis, on the other hand, the simulation run is repeated a number of times, each time with a different uncertainty scenario obtained using bootstrapping, parametric resampling, and/or re-calculation of uncertain values, yielding uncertainty distributions and confidence intervals for the nominal estimates.

Making the distinction between the nominal run and the uncertainty loops has the practical advantage that it allows the user to setup and evaluate complex simulations first using only the nominal runs to quickly obtain a picture of the results and identify possible errors in the data or in the model settings before running the more time-consuming uncertainty analysis loop.