Fast kriging of large data sets with Gaussian Markov random fields

作者：Hartman Linda^*; Hossjer Ola

来源：Computational Statistics & Data Analysis, 2008, 52(5): 2331-2349.

DOI：10.1016/j.csda.2007.09.018

• spatial interpolation
• Markov random field
• nonlattice data
• bilinear interpolation

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. Computation time and memory requirement can be an obstacle for kriging for data sets with many observations. Calculations are accelerated and memory requirements decreased by using a Gaussian Markov random field on a lattice as an approximation of a Gaussian field. The algorithms are well suited also for nonlattice data when exploiting a bilinear interpolation at nonlattice locations.

• 出版日期2008-1-20