Dynamic lattice-Markov spatio-temporal models for environmental data

This paper is concerned with the modelling of the latent structure of a Bayesian spatio-temporal model with a view to improving parameter inference, smoothing and prediction. The equilibrium distribution of a time stationary system will be examined, paying particular attention to edge-effects and the effect of grid-coarsening. In order to develop an effective MCMC algorithm, the latent process is integrated out of the model. These techniques will be illustrated using North Atlantic ocean temperature data.