Description
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.
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.
Copyright Statement
Details
Title
- A study of accelerated Bayesian additive regression trees
Contributors
- Yalov, Saar (Author)
- Hahn, P. Richard (Thesis advisor)
- McCulloch, Robert (Committee member)
- Kao, Ming-Hung (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2019
Subjects
Resource Type
Collections this item is in
Note
- Partial requirement for: M.S., Arizona State University, 2019Note typethesis
- Includes bibliographical references (pages 37-38)Note typebibliography
- Field of study: Statistics
Citation and reuse
Statement of Responsibility
by Saar Yalov