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Probabilistic models for integration error in the assessment of functional cardiac models
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Title: | Probabilistic models for integration error in the assessment of functional cardiac models |
Authors: | Oates, CJ Niederer, S Lee, A Briol, F-X Girolami, M |
Item Type: | Conference Paper |
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(dx) 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 ≈ 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. |
Issue Date: | 1-Dec-2017 |
Date of Acceptance: | 1-Sep-2017 |
URI: | http://hdl.handle.net/10044/1/67107 |
ISSN: | 1049-5258 |
Publisher: | NIPS Proceedings |
Start Page: | 110 |
End Page: | 118 |
Journal / Book Title: | Advances in Neural Information Processing Systems |
Volume: | 2017 |
Copyright Statement: | © 2017 Neural Information Processing Systems Foundation, Inc. |
Conference Name: | Neural Information Processing Systems |
Keywords: | 1701 Psychology 1702 Cognitive Science |
Publication Status: | Published |
Start Date: | 2017-12-04 |
Finish Date: | 2017-12-09 |
Conference Place: | Long Beach, California, USA |
Appears in Collections: | Mathematics Statistics |