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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Model-Based Interpolation, Prediction, and Approximation
verfasst von : Antonio Possolo
Erschienen in: Uncertainty Quantification in Scientific Computing
Verlag: Springer Berlin Heidelberg
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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.