A BAYESIAN-ANALYSIS OF KRIGING

This article is concerned with predicting for Gaussian random fields i n a way that appropriately deals with uncertainty in the covariance fu nction. To this end, we analyze the best linear unbiased prediction pr ocedure within a Bayesian framework. Particular attention is paid to t he treatment of parameters in the covariance structure and their effec t on the quality, both real and perceived, of the prediction. These id eas are implemented using topographical data from Davis.