Abstract
The technique of smoothing H function in a reproducing kernel Hilbert space given contaminated function values is reviewed from a Bayesian point of view and inter¬preted as estimation of an interpolation function. It is shown that this estimation can be performed as parameter estimation in an appropriate linear model. An explicit representa¬tion of the smoothing error is derived with respect to which problems of optimal design are discussed. The criterion for determining a smoothing parameter is justified by this approach
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