XBART: Accelerated Bayesian Additive Regression Trees
This incremental improvement accelerates BART for applications in social, behavioral, and health sciences where unstructured predictors and unmeasured variation are common.
The paper tackled the problem of slow posterior estimation in Bayesian additive regression trees (BART) by developing a modified version with a stochastic hill climbing algorithm, resulting in a method that is many times faster and less memory intensive while maintaining predictive accuracy, with simulation studies showing it is comparable in computation time and more accurate than random forests and gradient boosting.
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.