Speaker
Description
In this talk, the problem of selecting a set of design points for universal kriging,
which is a widely used technique for spatial data analysis, is further
investigated. The goal is to select the design points in order to make simultaneous
predictions of the random variable of interest at a finite number of
unsampled locations with maximum precision. Specifically, a correlated random
field given by a linear model with an unknown parameter vector and
a spatial error correlation structure is considered as response. A new design
criterion that aims at simultaneously minimizing the variation of the prediction
errors at various points is proposed. There is also presented an efficient
technique for incrementally building designs for that criterion scaling well
for high dimensions. Thus the method is particularly suitable for big data
applications in areas of spatial data analysis such as mining, hydrogeology,
natural resource monitoring, and environmental sciences or equivalently
for any computer simulation experiments. The effectiveness of the proposed
designs is demonstrated through a numerical example.
| Classification | Mainly methodology |
|---|---|
| Keywords | optimal experimental design, active learning, Gaussian process |