Sparse Log Gaussian Processes via MCMC for Spatial Epidemiology

Log Gaussian processes (LGP) are an attractive manner to construct intensity surfaces for the purposes of spatial epidemiology. The intensity surfaces are naturally smoothed by placing a GP prior over the relative log Poisson rate. In this work a fully independent training conditional (FITC) sparse approximation is used to speed up GP computations. The sampling of the latent values is sped up with transformations taking into account the approximate conditional posterior precision.