Department of Statistics Spring 2019 Seminar Series
Talk Title: xBART: Accelerated Bayesian Additive Regression Trees
Speaker: P. Richard Hahn, Associate Professor of Statistics, School of Mathematical and Statistical Sciences, Arizona State University
Time: 11:00am
Abstract: Bayesian additive regression trees (BART) 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.
Location: Department of Statistics, Room B02, 2006 Sheridan Rd, Evanston 60208
Kisa Kowal
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