Conventional geostatistical methodology solves the problem of predicting the realized value of a linear functional of a Gaussian spatial stochastic process S…x) based on observations Yi ˆ S…xi † ‡ Zi at sampling locations xi , where the Zi are mutually independent, zero-mean Gaussian random variables. We describe two spatial applications for which Gaussian distributional assumptions are clearly...

Sporisorium reilianum has caused significant economical damages in Mexico, in temperate and relatively dry areas, where maize is cultivated. The knowledge about the spatial distribution of this pathogen is basic to elaborate integrated management programs, and precise and eff icient the development of sampling methods and control techniques. Unfortunately, in Mexico there are no studies on spat...

The Quasi-Linear Geostatistical Approach is a method of inverse modeling to identify parameter fields, such as the hydraulic conductivity in heterogeneous aquifers, given observations of related quantities like hydraulic heads or arrival times of tracers. Derived in the Bayesian framework, it allows to rigorously quantify the uncertainty of the identified parameter field. Since inverse modeling...

The spatial prediction of point values from areal data of the same attribute is addressed within the general geostatistical framework of change of support; the term support refers to the domain informed by each datum or unknown value. It is demonstrated that the proposed geostatistical framework can explicitly and consistently account for the support differences between the available areal data...