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Prediction is a classic challenge in spatial statistics and the inclusion ofspatial covariates can greatly improve predictive performance when incorporatedinto a model with latent spatial effects. It is desirable to develop flexibleregression models that allow for nonlinearities and interactions in thecovariate structure. Machine learning models have been suggested in the spatialcontext, allowing for spatial dependence in the residuals, but fail to providereliable uncertainty estimates. In this paper, we investigate a novelcombination of a Gaussian process spatial model and a Bayesian AdditiveRegression Tree (BART) model. The computational burden of the approach isreduced by combining Markov chain Monte Carlo (MCMC) with the Integrated NestedLaplace Approximation (INLA) technique. We study the performance of the methodvia simulations and use the model to predict anthropometric responses,collected via household cluster samples in Kenya.

BART-SIMP: a novel framework for flexible spatial covariate modeling and prediction using Bayesian additive regression trees