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Model-Based Interpolation, Prediction, and Approximation



Antonio M. Possolo


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.
Uncertainty Quantification in Scientific Computing
Publisher Info
Springer, Philadelphia, PA


interpolation, prediction, approximation, uncertainty, influenza, greenhouse gases, projection pursuit.


Possolo, A. (2012), Model-Based Interpolation, Prediction, and Approximation, Uncertainty Quantification in Scientific Computing, Springer, Philadelphia, PA (Accessed June 14, 2024)


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Created January 1, 2012, Updated June 2, 2021