%0 Conference Proceedings
%T Model-Based Interpolation, Prediction, and Approximation
%+ Statistical Engineering Division [Gaithersburg]
%A Possolo, Antonio
%Z Part 4: UQ Practice
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 10th Working Conference on Uncertainty Quantification in Scientific Computing (WoCoUQ)
%C Boulder, CO, United States
%Y Andrew M. Dienstfrey
%Y Ronald F. Boisvert
%I Springer
%3 Uncertainty Quantification in Scientific Computing
%V AICT-377
%P 195-211
%8 2011-08-01
%D 2011
%R 10.1007/978-3-642-32677-6_13
%K interpolation
%K prediction
%K approximation
%K uncertainty
%K influenza
%K greenhouse gases
%K projection pursuit
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%2 https://inria.hal.science/hal-01518670/document
%2 https://inria.hal.science/hal-01518670/file/978-3-642-32677-6_13_Chapter.pdf
%L hal-01518670
%U https://inria.hal.science/hal-01518670
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC2
%~ IFIP-AICT-377
%~ IFIP-WOCOUQ
%~ IFIP-WG2-5