A preposterior analysis to predict identifiability in the experimental calibration of computer models

Paul D. Arendt, Daniel W. Apley*, Wei Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


When using physical experimental data to adjust, or calibrate, computer simulation models, two general sources of uncertainty that must be accounted for are calibration parameter uncertainty and model discrepancy. This is complicated by the well-known fact that systems to be calibrated are often subject to identifiability problems, in the sense that it is difficult to precisely estimate the parameters and to distinguish between the effects of parameter uncertainty and model discrepancy. We develop a form of preposterior analysis that can be used, prior to conducting physical experiments but after conducting the computer simulations, to predict the degree of identifiability that will result after conducting the physical experiments for a given experimental design. Specifically, we calculate the preposterior covariance matrix of the calibration parameters and demonstrate that, in the examples that we consider, it provides a reasonable prediction of the actual posterior covariance that is calculated after the experimental data are collected. Consequently, the preposterior covariance can be used as a criterion for designing physical experiments to help achieve better identifiability in calibration problems.

Original languageEnglish (US)
Pages (from-to)75-88
Number of pages14
JournalIIE Transactions (Institute of Industrial Engineers)
Issue number1
StatePublished - Jan 2 2016


  • Bayesian calibration
  • Gaussian process models
  • Kriging
  • computer experiments

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering


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