Strategies for Separation of Aleatory and Epistemic

Strategies for Separation of Aleatory and Epistemic
Separation of aleatory and epistemic uncertainty in
On the separation of aleatory and epistemic uncertainties
(PDF) Separation of Aleatory and Epistemic Uncertainty in
Strategies for separation of aleatory and epistemic
“This Is What We Don't Know”: Treating Epistemic
Separation of aleatory and epistemic uncertainty in
Separation of aleatory and epistemic uncertainty in
Perspectives on Verification, Validation, and Uncertainty
Separation of aleatory and epistemic uncertainty in

separation of aleatory and epistemic uncertainty in probabilistic model validation

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separation of aleatory and epistemic uncertainty in probabilistic model validation video

Validation, and Sensitivity Analysis: What’s What,” Reliability Engineering and System Safety, vol. 91, No. 10-11, pp. 1331-1357. • Oberkampf, W. L. and S. Ferson (2007), "Model Validation under Both Aleatory and Epistemic Uncertainty," NATO/RTO Symposium on Computational Uncertainty in Military Vehicle Design. Athens, Greece, NATO. AVT Mullins, Joshua, Ling, You, Mahadevan, Sankaran, Sun, Lin, and Strachan, Alejandro. Separation of aleatory and epistemic uncertainty in probabilistic model validation The objective of this paper is to demonstrate how the process of uncertainty designation (aleatory vs. epistemic) and subsequent separation in a two-staged nested Monte Carlo simulation approach Appropriate validation comparisons for different data scenarios are identified. • Point-by-point comparisons are used to separate aleatory and epistemic uncertainty. • An aggregation approach to obtain an overall metric across inputs is developed. • A method to include surrogate model uncertainty in validation results is proposed. Table 3 shows that the epistemic model uncertainty for moment capacity is dependent on the observed failure modes. For single failure modes, the reduction in total model uncertainty (expressed as ) due to the separation of CoV aleatory uncertainty is between 11-13%. In contrast, for the combined failure mode, aleatory Separation of Aleatory and Epistemic Uncertainty in Probabilistic Model Validation. In the presence of multiple uncertainty sources, model validation metrics that compare the distributions of Downloadable (with restrictions)! This paper investigates model validation under a variety of different data scenarios and clarifies how different validation metrics may be appropriate for different scenarios. In the presence of multiple uncertainty sources, model validation metrics that compare the distributions of model prediction and observation are considered. When predictive BNs are used, we recommend communicating that there is no clear separation between epistemic and aleatory uncertainty in the assessment. A predictive BN of assessment variables can be specified node by node, or for combinations of nodes, using statistical predictive inference outside the network or expert judgment. This enables aleatory and epistemic uncertainty sources to be separated from one another, which aids in decision making for uncertainty reduction when the model performance is inadequate. Additionally, understanding the reliability of the model as a function of the input may help to identify systematic inadequacies in model form. Practical applications for separation of aleatory and epistemic uncertainties are demonstrated with two examples applied to steel structures and pipelines. These examples show the need for separation of uncertainties, the application of existing strategies and their limitations. The first example pertains to the behaviour of a simple steel connection subjected to combined axial, shear and

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separation of aleatory and epistemic uncertainty in probabilistic model validation