Optimal Spatial Prediction with Kriging

Robert Nishihara Machine Learning, Statistics

[latexpage]Suppose we are modeling a spatial process (for instance, the amount of rainfall around the world, the distribution of natural resources, or the population density of an endangered species). We’ve measured the latent function $Z$ at some locations ${\bf s}_1, \ldots, {\bf s}_N$, and we’d like to predict the function’s value at some new location ${\bf s}_0$. Kriging is a technique for extrapolating our measurements to arbitrary locations. For an in-depth discussion, see¬†Cressie and Wikle (2011).¬†Here I derive Kriging in a simplified case. I will assume that $Z$ is an intrinsically stationary process. In other words, there exists some semivariogram $\gamma({\bf h})$ such that \[ \text{var}[Z({\bf s}+{\bf h}) – Z({\bf s})] = 2\gamma({\bf h}) . \] Furthermore, I will assume …