Evidential Model Validation under Epistemic Uncertainty
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-02-22
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
This paper proposes evidence theory based methods to both quantify the epistemic uncertainty and validate computational model.
Three types of epistemic uncertainty concerning input model data, that is, sparse points, intervals, and probability distributions with uncertain parameters, are considered.
Through the proposed methods, the given data will be described as corresponding probability distributions for uncertainty propagation in the computational model, thus, for the model validation.
The proposed evidential model validation method is inspired by the idea of Bayesian hypothesis testing and Bayes factor, which compares the model predictions with the observed experimental data so as to assess the predictive capability of the model and help the decision making of model acceptance.
Developed by the idea of Bayes factor, the frame of discernment of Dempster-Shafer evidence theory is constituted and the basic probability assignment (BPA) is determined.
Because the proposed validation method is evidence based, the robustness of the result can be guaranteed, and the most evidence-supported hypothesis about the model testing will be favored by the BPA.
The validity of proposed methods is illustrated through a numerical example.
American Psychological Association (APA)
Deng, Wei& Lu, Xi& Deng, Yong. 2018. Evidential Model Validation under Epistemic Uncertainty. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1208557
Modern Language Association (MLA)
Deng, Wei…[et al.]. Evidential Model Validation under Epistemic Uncertainty. Mathematical Problems in Engineering No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1208557
American Medical Association (AMA)
Deng, Wei& Lu, Xi& Deng, Yong. Evidential Model Validation under Epistemic Uncertainty. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1208557
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208557