** Robert E. McCulloch **

University of
Texas at Austin

Authors: Hugh A. Chipman (Acadia University), Edward I. George (University of Pennsylvania), and Robert E. McCulloch (University of Texas at Austin)

In BART: Bayesian Additive Regression Trees, we developed a general approach for fitting the model y = f(x) + e with minimimal assumptions about the structure of f. The function f is modeled as a sum of simple tree models. This sum is made meaningful by a Bayesian analysis with a simple yet powerful prior specification that provides for both overall regularization of the fit and the limiting of the contribution of each individual tree to the sum. An MCMC algorithm provides inference. The BART model is shown to have excellent out of sample predictive performance. Users however, often want more than just good predictions. In this talk we discuss ways to interpret the BART fit as well as ways to inject non-generic prior information. For example, users often want to know which variables are most important. How can this information be gleaned from the BART inference?