Fast kriging of large data sets with Gaussian Markov random fields
Linda Werner and Ola Hössjer
Centre for Mathematical Sciences
Mathematical Statistics
Lund Institute of Technology,
Lund University,
2004
ISSN 14039338

Abstract:

Spatial data sets are analysed in many scientific disciplines. Kriging, i.e.
minimum mean squared error linear prediction, is probably the most widely
used method of spatial prediction. For data sets with many observations,
computation time and memory requirement could be an obstacle for kriging.
In this article we approximate a Gaussian field with a Gaussian Markov random
field on a lattice to speed up calculations and decrease memory requirements.
By using a bilinear interpolation at nonlattice locations the method is
well suited for nonlattice data.



Key words:

spatial interpolation, GMRF, nonlattice data
