- Keywords: Gaussian random fields, kernel methods
- TL;DR: A kernel is defined, using Mercer series, on the half-plane for the purpose of better modeling temporal phenomena with a fixed beginning but no fixed end.
- Abstract: In spatial statistics, kriging models are often designed using a stationary covariance structure; this translation-invariance produces models which have numerous favorable properties. This assumption can be limiting, though, in circumstances where the dynamics of the model have a fundamental asymmetry, such as in modeling phenomena that evolve over time from a fixed initial profile. We propose a new nonstationary kernel which is only defined over the half-line to incorporate time more naturally in the modeling process.