ASBART:Accelerated Soft Bayes Additive Regression Trees

Hao Ran, Yang Bai
10/21/2023
stat.MLcs.LGstat.CO

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Abstract

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.

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Cite this paper

@article{ran2023asbartaccelerated,
  title  = {ASBART:Accelerated Soft Bayes Additive Regression Trees},
  author = {Hao Ran and Yang Bai},
  year   = {2023},
  eprint = {2310.13975},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.13975v1}
}

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