Bayesian model selection in spatial lattice models

This work describes a Bayesian approach for model selection in Gaussian conditional autoregressive models and Gaussian simultaneous autoregressive models which are commonly used to describe spatial lattice data. The approach is aimed at situations where all competing models have the same mean structure, but differ on some aspects of their covariance structures. The proposed approach uses as selection criterion the posterior model probabilities computed using some default priors for the model parameters. The proposed methodology is illustrated using two real datasets.