Abstract
Prediction of a random field based on observations of the random field at some set of locations arises in mining, hydrology, atmospheric sciences, and geography. Kriging, a prediction scheme defined as any prediction scheme that minimizes mean squared prediction error among some class of predictors under a particular model for the field, is commonly used in all these areas of prediction. This book summarizes past work and describes new approaches to thinking about kriging.
Cite
CITATION STYLE
Stein, M. L. (1999). Spatial Interpolation, some theory for kriging. Spring-Verlag (pp. 1–247). Springer Science & Business Media. Retrieved from http://books.google.com/books?id=5n_XuL2Wx1EC&printsec=frontcover&dq=Bayesian+nonstationary+spatial+model+for+very+large+datasets&hl=&cd=30&source=gbs_api%0Apapers3://publication/uuid/1D675683-445F-475E-AE79-6C60EA840639
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