preprint · arXiv (Cornell University) · 2022

Bayesian additive regression trees for probabilistic programming

Miriana Quiroga, Pablo Garay, Juan Manuel Alonso, Juan Martín Loyola, Osvaldo A. Martin

Cited by 7Open access

Abstract

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.

Cite this paper

APA 7

Quiroga, M., Garay, P., Alonso, J. M., Loyola, J. M., & Martin, O. A. (2022). Bayesian additive regression trees for probabilistic programming. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2206.03619

MLA 9

Quiroga, Miriana, et al. “Bayesian additive regression trees for probabilistic programming.” arXiv (Cornell University), 2022, https://doi.org/10.48550/arxiv.2206.03619.

Chicago

Quiroga, Miriana, Pablo Garay, Juan Manuel Alonso, Juan Martín Loyola, and Osvaldo A. Martin. 2022. “Bayesian additive regression trees for probabilistic programming.” arXiv (Cornell University). https://doi.org/10.48550/arxiv.2206.03619.

Harvard

Quiroga, M. et al. (2022) ‘Bayesian additive regression trees for probabilistic programming’, arXiv (Cornell University). Available at: https://doi.org/10.48550/arxiv.2206.03619.

BibTeX

@misc{quiroga2022bayesian,
  title = {{Bayesian additive regression trees for probabilistic programming}},
  author = {Quiroga, Miriana and Garay, Pablo and Alonso, Juan Manuel and Loyola, Juan Martín and Martin, Osvaldo A.},
  journal = {arXiv (Cornell University)},
  year = {2022},
  doi = {10.48550/arxiv.2206.03619}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.