  Bayesian additive regression trees (BART) is a non-parametric method to
approximate functions. It is a black-box method based on the sum of many trees
where priors are used to regularize inference, mainly by restricting trees'
learning capacity so that no individual tree is able to explain the data, but
rather the sum of trees. We discuss BART in the context of probabilistic
programming languages (PPL), i.e., we present BART as a primitive that can be
used as a component of a probabilistic model rather than as a standalone model.
Specifically, we introduce the Python library PyMC-BART, which works by
extending PyMC, a library for probabilistic programming. We showcase a few
examples of models that can be built using PyMC-BART, discuss recommendations
for the selection of hyperparameters, and finally, we close with limitations of
our implementation and future directions for improvement.
