Paun, L. M., Colebank, M. J., Olufsen, M. S., Hill, N. A. and Husmeier, D. (2020) Assessing model mismatch and model selection in a Bayesian uncertainty quantification analysis of a fluid-dynamics model of pulmonary blood circulation. Journal of the Royal Society: Interface, 17(173), 20200886. (doi: 10.1098/rsif.2020.0886) (PMID:33353505)
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Abstract
This study uses Bayesian inference to quantify the uncertainty of model parameters and haemodynamic predictions in a one-dimensional pulmonary circulation model based on an integration of mouse haemodynamic and micro-computed tomography imaging data. We emphasize an often neglected, though important source of uncertainty: in the mathematical model form due to the discrepancy between the model and the reality, and in the measurements due to the wrong noise model (jointly called ‘model mismatch’). We demonstrate that minimizing the mean squared error between the measured and the predicted data (the conventional method) in the presence of model mismatch leads to biased and overly confident parameter estimates and haemodynamic predictions. We show that our proposed method allowing for model mismatch, which we represent with Gaussian processes, corrects the bias. Additionally, we compare a linear and a nonlinear wall model, as well as models with different vessel stiffness relations. We use formal model selection analysis based on the Watanabe Akaike information criterion to select the model that best predicts the pulmonary haemodynamics. Results show that the nonlinear pressure–area relationship with stiffness dependent on the unstressed radius predicts best the data measured in a control mouse.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Paun, Dr Mihaela and Hill, Professor Nicholas and Husmeier, Professor Dirk |
| Authors: | Paun, L. M., Colebank, M. J., Olufsen, M. S., Hill, N. A., and Husmeier, D. |
| College/School: | College of Science and Engineering > School of Mathematics and Statistics > Mathematics College of Science and Engineering > School of Mathematics and Statistics > Statistics |
| Journal Name: | Journal of the Royal Society: Interface |
| Publisher: | The Royal Society |
| ISSN: | 1742-5689 |
| ISSN (Online): | 1742-5662 |
| Published Online: | 23 December 2020 |
| Copyright Holders: | Copyright © 2020 The Authors |
| First Published: | First published in Journal of the Royal Society: Interface 2020 |
| Publisher Policy: | Reproduced under a Creative Commons License |
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