Model-Based Interpolation, Prediction, and Approximation

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.
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Andrew M. Dienstfrey; Ronald F. Boisvert. 10th Working Conference on Uncertainty Quantification in Scientific Computing (WoCoUQ), Aug 2011, Boulder, CO, United States. Springer, IFIP Advances in Information and Communication Technology, AICT-377, pp.195-211, 2012, Uncertainty Quantification in Scientific Computing. 〈10.1007/978-3-642-32677-6_13〉
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Antonio Possolo. Model-Based Interpolation, Prediction, and Approximation. Andrew M. Dienstfrey; Ronald F. Boisvert. 10th Working Conference on Uncertainty Quantification in Scientific Computing (WoCoUQ), Aug 2011, Boulder, CO, United States. Springer, IFIP Advances in Information and Communication Technology, AICT-377, pp.195-211, 2012, Uncertainty Quantification in Scientific Computing. 〈10.1007/978-3-642-32677-6_13〉. 〈hal-01518670〉

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