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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

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.

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Date Created
  • 2019
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  • Text
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    Note
    • Partial requirement for: M.S., Arizona State University, 2019
      Note type
      thesis
    • Includes bibliographical references (pages 37-38)
      Note type
      bibliography
    • Field of study: Statistics

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    by Saar Yalov

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