  Bayesian additive regression trees (BART) (Chipman et. al., 2010) is a
powerful predictive model that often outperforms alternative models at
out-of-sample prediction. BART is especially well-suited to settings with
unstructured predictor variables and substantial sources of unmeasured
variation as is typical in the social, behavioral and health sciences. This
paper develops a modified version of BART that is amenable to fast posterior
estimation. We present a stochastic hill climbing algorithm that matches the
remarkable predictive accuracy of previous BART implementations, but is many
times faster and less memory intensive. Simulation studies show that the new
method is comparable in computation time and more accurate at function
estimation than both random forests and gradient boosting.
