Space time problems and applications

We consider Markov random field models, which are spatial models applicable in e.g. agricultural experiments and image analysis. Recently, Gaussian Markov random field models were expressed as state space models, enabling the Kalman filter machinery. We use this connection to extend the Markov random field models by generalising the corresponding state space model. It turns out that several non-Gaussian spatial models can be analysed by combining approximate Kalman filter techniques with importance sampling.