Spatio-temporal Stochastic Models

The research on spatio-temporal stochastic models has a long history, starting in the late nineteen sixties. The main themes are theoretical, computational, and applied studies of stochastic models in space and time, adapted for different applications. Laplace random fields is a general class of stochastic models well suited as a flexible non-Gaussian alternative to Gaussian spectral models. Another class of models are Markov random fields based on stochastic partial differential equations, allowing highly efficient simulation and estimation algorithms. A third group of models are of Lagrange type, which consist of random perturbations of Gaussian fields.

Applications are in statistical extreme value analysis, interpolation and extrapolation of environment data, random fatigue and risk analysis, statistical computation with oceanographic applications.