Wei, Pengfei, Liu, Fuchao, Valdebenito, Marcos and Beer, Michael 
ORCID: 0000-0002-0611-0345
(2021)
Bayesian probabilistic propagation of imprecise probabilities with large epistemic uncertainty.
Mechanical Systems and Signal Processing, 149.
p. 107219.
|
Text
manuscript_R1.pdf - Author Accepted Manuscript Download (2MB) | Preview |
Abstract
Efficient propagation of imprecise probability models is one of the most important, yet challenging tasks, for uncertainty quantification in many areas and engineering practices, especially when the involved epistemic uncertainty is substantial due to the extreme lack of information. In this work, a new methodology framework, named as “Non-intrusive Imprecise Probabilistic Integration (NIPI)”, is developed for achieving the above target, and specifically, the distributional probability-box model and the estimation of the corresponding probabilistic moments of model responses are of concern. The NIPI owns two attractive characters. First, the spatial correlation information in both aleatory and epistemic uncertainty spaces, revealed by the Gaussian Process Regression (GPR) model, is fully integrated for deriving NIPI estimations of high accuracy by using Bayesian inference. Second, the numerical errors are regarded as a kind of epistemic uncertainty, by analytically propagating them, the posterior variances are derived for indicating the errors of the NIPI estimations. Further, an adaptive experiment design strategy is developed to accelerate the convergence of NIPI by making full use of the information of “contribution to posterior variance” revealed by the GPR model. The performance of the proposed methods is demonstrated by numerical and engineering examples.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Uncertainty quantification, Bayesian inference, Probabilistic integration, Imprecise probabilities, Gaussian process regression, Epistemic uncertainty, Active learning |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 07 Sep 2020 08:34 |
| Last Modified: | 18 Jan 2023 23:35 |
| DOI: | 10.1016/j.ymssp.2020.107219 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3100132 |
Dimensions
Dimensions
