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45th Annual Summer Institute of Applied Statistics 2023

Bayesian Additive Regression Trees: An Introduction and R Tutorial



June 21 - 22, 2023

Dr. Robert McCulloch
We are honored to have Dr. Robert McCulloch, a Professor at Arizona State University as our presenter at this years conference.





Abstract

Modern computing power has led to a breakthrough in our ability to learn high-dimensional, complex relationships from data. Perhaps the two key modeling approaches that have led to real breakthroughs in application are the ones based on neural networks and the ones based on ensembles of trees. In this course, we learn a Bayesian approach to modeling with ensembles of trees. The basic approach is called BART for Bayesian Additive Regression Trees. The Bayesian approach allows for Markov Chain Monte Carlo stochastic exploration of the model space, uncertainty quantification, and Bayesian model elaboration. BART is one of the few model approaches which is able to exploit the powerful Bayesian conceptual toolkit.

This BART tutorial will consist of
(a) Review of basic tree modeling, random forests, and boosting.
(b) Basic overview of BART with tutorial on using the R package BART.
(c) Under the hood: how does BART work?
(d) Recent advances in Bayesian ensemble modeling.

To view past presenters, click here.

For questions regarding SIAS, contact Kimri Mansfield at (801) 422-4506 or kmansfield@stat.byu.edu.