There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
Gaussian random fields (GRFs) constitute an important part of spatial modelling, but
can be computationally infeasible for general covariance structures. An efficient
approach is to specify GRFs via stochastic partial differential equations (SPDEs)
and derive Gaussian Markov random field (GMRF) approximations of the solutions. We
consider the construction of a class of non-stationary GRFs with varying local anisotropy,
where the local anisotropy is introduced by allowing the coefficients in the SPDE
to vary with position. Specifically, using a form of diffusion equation driven by
Gaussian white noise with a diffusion matrix that varies with position. This allows
for the introduction of parameters that control the GRF by parametrizing the diffusion
matrix. These parameters and the GRF may be considered to be part of a hierarchical
model and the parameters estimated in a Bayesian framework. The results show that
the use of an SPDE with non-constant coefficients is a useful way of creating non-stationary
spatial GMRFs that allows for physical interpretability of the parameters.