ASBART:Accelerated Soft Bayes Additive Regression Trees
Hao Ran Yang Bai
Bayes additive regression trees(BART) is a nonparametric regression model
which has gained wide-spread popularity in recent years due to its flexibility
and high accuracy of estimation. Soft BART,one variation of BART,improves both
practically and heoretically on existing Bayesian sum-of-trees models. One
bottleneck for Soft BART is its slow speed in the long MCMC loop. Compared to
BART,it use more than about 20 times to complete the calculation with the
default setting. We proposed a variant of BART named accelerate Soft
BART(ASBART). Simulation studies show that the new method is about 10 times
faster than the Soft BART with comparable accuracy. Our code is open-source and
available at https://github.com/richael008/XSBART.