An Equivalence between SILF-SVR and Ordinary Kriging
Neural Processing Letters
Survey paper: Optimal experimental design and some related control problems
Automatica (Journal of IFAC)
An efficient methodology for modeling complex computer codes with Gaussian processes
Computational Statistics & Data Analysis
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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.