ABSTRACT

We consider prediction and uncertainty characterization for systems which are approximated using complex mathematical models. A Bayesian calibration and forecasting approach is given which allows for model calibration while incorporating various sources of uncertainty. This includes uncertainty in the fitted parameters as well as in the discrepancy between observed data and model predictions from even the best-fitting parameter values.

We'll consider applications involving, hydrology, charged particle accelerators, and aircraft safety.