Bayesian Additive Regression Trees using Bayesian model averaging

@article{Hernndez2018BayesianAR,
  title={Bayesian Additive Regression Trees using Bayesian model averaging},
  author={Belinda Hern{\'a}ndez and Adrian E. Raftery and Stephen R Pennington and Andrew C. Parnell},
  journal={Statistics and Computing},
  year={2018},
  volume={28},
  pages={869-890}
}
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 the individual trees are the base learners. However for datasets where the number of variables p is large (e.g. p > 5, 000) the algorithm can become prohibitively expensive, computationally. Another method which is popular for high dimensional data is random forests, a machine learning algorithm which grows trees using a greedy… CONTINUE READING