Abstract
Simple kriging and universal kriging constitute two extremes in geostatistical linear prediction theory. The former requires the expected function to be known, the latter assumes nothing about the parameters involved in the expected function. The Bayesian approach allows the user to specify prior knowledge about model parameters as a qualified guess with uncertainties. This may be done in cases where the parameters have physical meaning. Two versions of Bayesian approaches to kriging are defined. It is demonstrated that one version defines a continuum of models between simple and universal kriging. Two examples related to seismic depth conversion is included.
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