In this article, we introduce the BART R package which is an acronym for Bayesian additive
regression trees. BART is a Bayesian nonparametric, machine learning, ensemble …

Bayesian additive regression trees (BART) is a Bayesian approach to flexible nonlinear
regression which has been shown to be competitive with the best modern predictive …

We present a new package in R implementing Bayesian additive regression trees (BART).
The package introduces many new features for data analysis using BART such as variable …

Abstract Bayesian Additive Regression Trees (BART) is a statistical sum of trees model. It
can be considered a Bayesian version of machine learning tree ensemble methods where …

Bayesian additive regression trees (BART)(Chipman et. al., 2010) is a powerful predictive
model that often outperforms alternative models at out-of-sample prediction. BART is …

Ensembles of decision trees are a useful tool for obtaining flexible estimates of regression
functions. Examples of these methods include gradient-boosted decision trees, random …

Bayesian additive regression trees (BART) provides a flexible approach to fitting a variety of
regression models while avoiding strong parametric assumptions. The sum-of-trees model is …

We develop a Bayesian “sum-of-trees” model where each tree is constrained by a
regularization prior to be a weak learner, and fitting and inference are accomplished via an …

Bayesian additive regression trees (BART) has become increasingly popular as a flexible
and scalable nonparametric regression approach for modern applied statistics problems …

Bayesian additive regression trees (BART) provide a framework for flexible nonparametric
modeling of relationships of covariates to outcomes. Recently, BART models have been …