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. / Dissertation/Thesis / Masters Thesis Statistics 2019
| Identifer | oai:union.ndltd.org:asu.edu/item:53698 |
| Date | January 2019 |
| Contributors | Yalov, Saar (Author), Hahn, P. Richard (Advisor), McCulloch, Robert (Committee member), Kao, Ming-Hung (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 44 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/ |
Page generated in 0.0016 seconds