Abstract : Model-based interpolation, prediction, and approximation are contingent on the choice of model: since multiple alternative models typically can reasonably be entertained for each of these tasks, and the results are correspondingly varied, this often is a considerable source of uncertainty. Several statistical methods are illustrated that can be used to assess the contribution that this uncertainty component makes to the uncertainty budget: when interpolating concentrations of greenhouse gases over Indianapolis, predicting the viral load in a patient infected with influenza A, and approximating the solution of the kinetic equations that model the progression of the infection.
https://hal.inria.fr/hal-01518670 Contributor : Hal IfipConnect in order to contact the contributor Submitted on : Friday, May 5, 2017 - 10:55:54 AM Last modification on : Friday, May 5, 2017 - 10:57:10 AM Long-term archiving on: : Sunday, August 6, 2017 - 12:20:18 PM
Antonio Possolo. Model-Based Interpolation, Prediction, and Approximation. 10th Working Conference on Uncertainty Quantification in Scientific Computing (WoCoUQ), Aug 2011, Boulder, CO, United States. pp.195-211, ⟨10.1007/978-3-642-32677-6_13⟩. ⟨hal-01518670⟩