Quantifying the predictive uncertainty of complex numerical models

Abstract

The overall uncertainty of a model prediction is a combination of the uncertainty of the input parameters and the uncertainty of the model assumptions. The former is referred to as parameter uncertainty; the latter model uncertainty. A method for quantifying model uncertainty is proposed for complicated numerical models that are not amenable to more traditional approaches. The method is based solely on comparisons of model predictions with experimental measurements, with the difference reported in terms of only two metrics, a bias factor and a relative standard deviation. The simplicity of the approach makes it ideal for models used for regulatory compliance because approving authorities often lack detailed training in modelling and uncertainty analysis.