Probabilistic models for integration error in the assessment of functional cardiac models

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
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