BayesTree
swMATH ID:  7995 
Software Authors:  Hugh Chipman, Robert McCulloch 
Description:  BayesTree: Bayesian Methods for Tree Based Models: Implementation of BART: Bayesian Additive Regression Trees. We develop a Bayesian “sumoftrees” model where each tree is constrained by a regularization prior to be a weak learner, and fitting and inference are accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from a posterior. Effectively, BART is a nonparametric Bayesian regression approach which uses dimensionally adaptive random basis elements. Motivated by ensemble methods in general, and boosting algorithms in particular, BART is defined by a statistical model: a prior and a likelihood. This approach enables full posterior inference including point and interval estimates of the unknown regression function as well as the marginal effects of potential predictors. By keeping track of predictor inclusion frequencies, BART can also be used for modelfree variable selection. BART’s many features are illustrated with a bakeoff against competing methods on 42 different data sets, with a simulation experiment and on a drug discovery classification problem. 
Homepage:  http://cran.rproject.org/web/packages/BayesTree/index.html 
Dependencies:  R 
Related Software:  BartPy; R; bartMachine; glmnet; randomForest; UCIml; ElemStatLearn; tgp; AdaBoost.MH; EGO; gbm; mgcv; dbarts; CRAN; SemiPar; gss; BayesDA; C4.5; rpart; FindIt 
Referenced in:  59 Publications 
Standard Articles
1 Publication describing the Software, including 1 Publication in zbMATH  Year 

BART: Bayesian additive regression trees. Zbl 1189.62066 Chipman, Hugh A.; George, Edward I.; McCulloch, Robert E. 
2010

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Referenced by 132 Authors
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Referenced in 21 Serials
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