pith. sign in

arxiv: 1206.0622 · v2 · pith:BJWPNOPQnew · submitted 2012-06-04 · 📊 stat.ME

Spatial Mat\'ern fields driven by non-Gaussian noise

classification 📊 stat.ME
keywords fieldsnon-gaussianrandomgaussianmodelspdesusedaccurate
0
0 comments X
read the original abstract

The article studies non-Gaussian extensions of a recently discovered link between certain Gaussian random fields, expressed as solutions to stochastic partial differential equations (SPDEs), and Gaussian Markov random fields. The focus is on non-Gaussian random fields with Mat\'ern covariance functions, and in particular we show how the SPDE formulation of a Laplace moving average model can be used to obtain an efficient simulation method as well as an accurate parameter estimation technique for the model. This should be seen as a demonstration of how these techniques can be used, and generalizations to more general SPDEs are readily available.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.