A systematic approach to model validation based on Bayesian updates and prediction related rejection criteria
This work describes a solution to the validation challenge problem posed at the SANDIA Validation Challenge Workshop, May 2123, 2006, NM. It presents and applies a general methodology to it. The solution entails several standard steps, namely selecting and fitting several models to the available prior information and then sequentially rejecting those which do not perform satisfactorily in the validation and accreditation experiments. The rejection procedures are based on Bayesian updates, where the prior density is related to the current candidate model and the posterior density is obtained by conditioning on the validation and accreditation experiments. The result of the analysis is the computation of the failure probability as well as a quantification of the confidence in the computation, depending on the amount of available experimental data.
model validation bayesian updates failure probability uncertainty quantification