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A Study of Accelerated Bayesian Additive Regression Trees

Abstract Bayesian Additive Regression Trees (BART) is a non-parametric Bayesian model

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.
Created Date 2019
Contributor Yalov, Saar (Author) / Hahn, P. Richard (Advisor) / McCulloch, Robert (Committee member) / Kao, Ming-Hung (Committee member) / Arizona State University (Publisher)
Subject Statistics / Computer science / BART / Bayesian / Learn / Machine / Tree / XBART
Type Masters Thesis
Extent 44 pages
Language English
Note Masters Thesis Statistics 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis