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Model-Based Interpolation, Prediction, and Approximation
Published
Author(s)
Antonio M. Possolo
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.
Citation
Uncertainty Quantification in Scientific Computing
Possolo, A.
(2012),
Model-Based Interpolation, Prediction, and Approximation, Uncertainty Quantification in Scientific Computing, Springer, Philadelphia, PA
(Accessed October 16, 2025)