BAYESIAN GAUSSIAN MODELS FOR POINT REFERENCED SPATIAL AND SPATIO-TEMPORAL DATA

Abstract

When data is correlated both spatially and temporally, spatial and spatio-temporal modelling
is useful for meaningful interpretation of the parameters of the covariates and for
reliable predictions. In this paper we discuss some modelling strategies for point referenced
spatial and spatio-temporal data. We describe Gaussian models in this context and
use Bayesian hierarchical approaches for model based inference and predictions through
the Markov chain Monte Carlo (MCMC) algorithm. Yearly average precipitation data from
Western Australia is used to illustrate the models.