  In this paper we extend existing Bayesian methods for variable selection in
Gaussian process regression, to select both the regression terms and the active
covariates in the spatial correlation structure. We then use the estimated
posterior probabilities to choose between relatively few modes through
cross-validation, and consequently improve prediction.
