Comparison between Bayesian and interval uncertainty quantification: application to the AIRMOD test structure

Abstract

This paper concerns the comparison of two inverse methods for the quantification of uncertain model parameters, based on independent measurement data of the model’s responses. Specifically, Bayesian inference is compared to a novel method for the quantification of multivariate interval uncertainty. This comparison is made by applying both methods to the AIRMOD measurement data set, and comparing their results critically in terms of obtained information and computational expense. It is found that the results of the Bayesian identification provide less over-conservative bounds on the uncertainty in the responses of the AIRMOD model. Smthing about computational cost.