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Towards a Bayesian framework for model validation

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dc.contributor.authorGray, Ander-
dc.contributor.authorPatelli, Edoardo-
dc.date.accessioned2019-05-14T03:07:55Z-
dc.date.available2019-05-14T03:07:55Z-
dc.date.issued2019-05-26-
dc.identifier.citation13th International Conference on Applications of Statistics and Probability in Civil Engineering(ICASP13), Seoul, South Korea, May 26-30, 2019-
dc.identifier.isbn979-11-967125-0-1-
dc.identifier.otherICASP13-347-
dc.identifier.urihttps://hdl.handle.net/10371/153480-
dc.description.abstractIn this paper we discuss some concepts and a methodology of a Bayesian framework for model validation under uncertainty, which produces a probabilistic value for a models validity and may be used in the design of validation experiments. By using a stochastic metric as a measure of the distance between experiment and prediction, we update a validation distribution. We show this in practice using a simple numerical experiment and discuss the current shortcomings of the method. We finally discuss the role of information entropy in designing validation experiments.-
dc.language.isoen-
dc.titleTowards a Bayesian framework for model validation-
dc.typeConference Paper-
dc.identifier.doi10.22725/ICASP13.347-
dc.sortNo653-
dc.citation.pages1761-1768-
Appears in Collections:
College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Civil & Environmental Engineering (건설환경공학부)ICASP13
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