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Robust calibration of numerical models based on relative regret

Authors
  • Trappler, Victor
  • Arnaud, Elise
  • Vidard, Arthur
  • Debreu, Laurent
Publication Date
Feb 03, 2020
Source
HAL-SHS
Keywords
Language
English
License
Unknown
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Abstract

Classical methods of parameter estimation usually imply the minimisation of an objective function, that measures the error between some observations and the results obtained by a numerical model. In the presence of random inputs, the objective function becomes a random variable, and notions of robustness have to be introduced. In this paper, we are going to present how to take into account those uncertainties by defining a family of calibration objectives based on the notion of relative-regret with respect to the best attainable performance given the uncertainties and compare it with the minimum in the mean sense, and the minimum of variance.

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