A critical review of statistical calibration/prediction models handling data inconsistency and model inadequacy

Abstract

Inference of physical parameters from reference data is a well studied problem with many intricacies (inconsistent sets of data due to experimental systematic errors, approximate physical models...). The complexity is further increased when the inferred parameters are used to make predictions (virtual measurements) because parameters uncertainty has to be estimated in addition to parameters best value. The literature is rich in statistical models for the calibration/prediction problem, each having benefits and limitations. We review and evaluate standard and state-of-the-art statistical models in a common bayesian framework, and test them on synthetic and real datasets of temperature-dependent viscosity for the calibration of Lennard-Jones parameters of a Chapman-Enskog model.

References in corpus (3)

• Systematic Error Estimation for Chemical Reaction Energies
• Validating Predictions of Unobserved Quantities
• The parameters uncertainty inflation fallacy

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• Molecule-specific Uncertainty Quantification in Quantum Chemical Studies
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