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Statistical calibration of computer simulations

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  • Campbell, Katherine

Abstract

This paper surveys issues associated with the statistical calibration of physics-based computer simulators. Even in solidly physics-based models there are usually a number of parameters that are suitable targets for calibration. Statistical calibration means refining the prior distributions of such uncertain parameters based on matching some simulation outputs with data, as opposed to the practice of “tuning†or point estimation that is commonly called calibration in non-statistical contexts. Older methods for statistical calibration are reviewed before turning to recent work in which the calibration problem is embedded in a Gaussian process model. In procedures of this type, parameter estimation is carried out simultaneously with the estimation of the relationship between the calibrated simulator and truth.

Suggested Citation

  • Campbell, Katherine, 2006. "Statistical calibration of computer simulations," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1358-1363.
    • Handle: RePEc:eee:reensy:v:91:y:2006:i:10:p:1358-1363
    DOI: 10.1016/j.ress.2005.11.032
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    References listed on IDEAS

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    1. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    2. Craig P. S & Goldstein M. & Rougier J. C & Seheult A. H, 2001. "Bayesian Forecasting for Complex Systems Using Computer Simulators," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 717-729, June.
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