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• W2608260345 abstract "This paper studies the numerical computation of integrals, representing estimates or predictions, over the output $f(x)$ of a computational model with respect to a distribution $p(mathrm{d}x)$ over uncertain inputs $x$ to the model. For the functional cardiac models that motivate this work, neither $f$ nor $p$ possess a closed-form expression and evaluation of either requires $approx$ 100 CPU hours, precluding standard numerical integration methods. Our proposal is to treat integration as an estimation problem, with a joint model for both the a priori unknown function $f$ and the a priori unknown distribution $p$. The result is a posterior distribution over the integral that explicitly accounts for dual sources of numerical approximation error due to a severely limited computational budget. This construction is applied to account, in a statistically principled manner, for the impact of numerical errors that (at present) are confounding factors in functional cardiac model assessment." @default.
• W2608260345 created "2017-05-05" @default.
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• W2608260345 date "2016-06-22" @default.
• W2608260345 modified "2023-09-27" @default.
• W2608260345 title "Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models" @default.
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• W2608260345 hasPublicationYear "2016" @default.
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