Spatial mapping with Gaussian processes and nonstationary Fourier features
The use of covariance kernels is ubiquitous in the field of spatial statistics. Kernels allow data to be mapped into high-dimensional feature spaces and can thus extend simple linear additive methods to nonlinear methods with higher order interactions. However, until recently, there has been a stron...
| Main Authors: | , , , |
|---|---|
| Format: | Journal article |
| Published: |
Elsevier
2018
|