bartBMA: Bayesian Additive Regression Trees using Bayesian Model Averaging

"BART-BMA Bayesian Additive Regression Trees using Bayesian Model Averaging" (Hernandez B, Raftery A.E., Parnell A.C. (2018) <doi:10.1007/s11222-017-9767-1>) is an extension to the original BART sum-of-trees model (Chipman et al 2010). BART-BMA differs to the original BART model in two main aspects in order to implement a greedy model which will be computationally feasible for high dimensional data. Firstly BART-BMA uses a greedy search for the best split points and variables when growing decision trees within each sum-of-trees model. This means trees are only grown based on the most predictive set of split rules. Also rather than using Markov chain Monte Carlo (MCMC), BART-BMA uses a greedy implementation of Bayesian Model Averaging called Occam's Window which take a weighted average over multiple sum-of-trees models to form its overall prediction. This means that only the set of sum-of-trees for which there is high support from the data are saved to memory and used in the final model.

Getting started

Package details

AuthorBelinda Hernandez [aut, cre] Adrian E. Raftery [aut] Stephen R Pennington [aut] Andrew C. Parnell [aut] Eoghan O'Neill [ctb]
MaintainerBelinda Hernandez <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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bartBMA documentation built on March 13, 2020, 5:06 p.m.