Model-Based Interpolation, Prediction, and Approximation

Published

January 1, 2012

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

Publisher Info

Springer, Philadelphia, PA

Pub Type

Book Chapters

Keywords

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

Citation

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