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157274 https://hdl.handle.net/2286/R.I.53698 http://rightsstatements.org/vocab/InC/1.0/ 2019 iv, 38 pages Masters Thesis Academic theses Text eng Yalov, Saar Hahn, P. Richard McCulloch, Robert Kao, Ming-Hung Arizona State University Partial requirement for: M.S., Arizona State University, 2019 Includes bibliographical references (pages 37-38) Field of study: Statistics Bayesian Additive Regression Trees (BART) is a non-parametric Bayesian model<br/><br/>that often outperforms other popular predictive models in terms of out-of-sample error. This thesis studies a modified version of BART called Accelerated Bayesian Additive Regression Trees (XBART). The study consists of simulation and real data experiments comparing XBART to other leading algorithms, including BART. The results show that XBART maintains BART’s predictive power while reducing its computation time. The thesis also describes the development of a Python package implementing XBART. Statistics Computer Science BART Bayesian Learn Machine Tree XBART Bayesian statistical decision theory Regression Analysis Decision Trees Computational complexity A study of accelerated Bayesian additive regression trees