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Intrinsic Kriging and prior information

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  • E. Vazquez
  • E. Walter
  • G. Fleury

Abstract

Kriging, one of the oldest prediction methods based on reproducing kernels, can be used to build black‐box models in engineering. In practice, however, it is often necessary to take into account prior information to obtain satisfactory results. First, the kernel (the covariance) can be used to specify properties of the prediction such as its regularity or the correlation distance. Moreover, intrinsic Kriging (viewed as a semi‐ parametric formulation of Kriging) can be used with an additional set of factors to take into account a specific type of prior information. We show that it is thus very easy to transform a black‐box model into a grey‐box one. The prediction error is orthogonal in some sense to the prior information that has been incorporated. An application in flow measurement illustrates the interest of the method. Copyright © 2005 John Wiley & Sons, Ltd.

Suggested Citation

  • E. Vazquez & E. Walter & G. Fleury, 2005. "Intrinsic Kriging and prior information," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 21(2), pages 215-226, March.
    • Handle: RePEc:wly:apsmbi:v:21:y:2005:i:2:p:215-226
    DOI: 10.1002/asmb.536
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    Cited by:

    1. Ehsan Mehdad & Jack P.C. Kleijnen, 2018. "Stochastic intrinsic Kriging for simulation metamodeling," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 34(3), pages 322-337, May.
    2. Marrel, Amandine & Iooss, Bertrand & Laurent, Béatrice & Roustant, Olivier, 2009. "Calculations of Sobol indices for the Gaussian process metamodel," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 742-751.
    3. Marrel, Amandine & Iooss, Bertrand & Van Dorpe, François & Volkova, Elena, 2008. "An efficient methodology for modeling complex computer codes with Gaussian processes," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4731-4744, June.

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