Towards a Bayesian framework for model validation

Ander Gray, Edoardo Patelli

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Abstract

In 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.

Original languageEnglish
Number of pages8
DOIs
Publication statusPublished - 26 May 2019
Event13th International Conference on Applications of Statistics and Probability in Civil Engineering - Seoul, Korea, Republic of
Duration: 26 May 201930 May 2019

Conference

Conference13th International Conference on Applications of Statistics and Probability in Civil Engineering
Abbreviated titleICASP 13
CountryKorea, Republic of
CitySeoul
Period26/05/1930/05/19

Keywords

  • Bayesian framework
  • uncertainty
  • model validation

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    Gray, A., & Patelli, E. (2019). Towards a Bayesian framework for model validation. Paper presented at 13th International Conference on Applications of Statistics and Probability in Civil Engineering, Seoul, Korea, Republic of. https://doi.org/10.22725/ICASP13.347