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Validation and error estimation of computational models
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DOI：10.1016/j.ress.2005.11.035.png)
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This paper develops a Bayesian methodology for assessing the confidence in model prediction by comparing the model output with experimental data when both are stochastic. The prior distribution of the response is first computed, which is then updated based on experimental observation using Bayesian analysis to compute a validation metric. A model error estimation methodology is then developed to include model form error, discretization error, stochastic analysis error (UQ error), input data error and output measurement error. Sensitivity of the validation metric to various error components and model parameters is discussed. A numerical example is presented to illustrate the proposed methodology. (c) 2005 Published by Elsevier Ltd.
Keyword:
Bayesian statistics
error estimation
sensitivity
uncertainty
validation
verification
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11
论文数:
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被引数:
4.2W
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