Accelerated Bayesian Additive Regression Trees

Although less widely known than random forests or boosted regression trees, Bayesian additive regression trees (BART) \citep{chipman2010bart} is a powerful predictive model that often outperforms those better-known alternatives 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 orders of magnitude faster and uses a fraction of the memory. 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.