Bayesian additive regression trees and the General BART model. (28th August 2019)

• Record Type: Journal Article
• Authors: Tan, Yaoyuan Vincent; Roy, Jason
• Is Part Of: Statistics in medicine. Volume 38:Number 25(2019)
• Page Start: 5048
• Page End: 5069
• Publication Date: 2019-08-28
• Subjects: Bayesian nonparametrics -- Dirichlet process mixtures -- machine learning -- semiparametric models -- spatial; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8347
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : Bayesian additive regression trees (BART) is a flexible prediction model/machine learning approach that has gained widespread popularity in recent years. As BART becomes more mainstream, there is an increased need for a paper that walks readers through the details of BART, from what it is to why it works. This tutorial is aimed at providing such a resource. In addition to explaining the different components of BART using simple examples, we also discuss a framework, the General BART model that unifies some of the recent BART extensions, including semiparametric models, correlated outcomes, and statistical matching problems in surveys, and models with weaker distributional assumptions. By showing how these models fit into a single framework, we hope to demonstrate a simple way of applying BART to research problems that go beyond the original independent continuous or binary outcomes framework.