29
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      BART: Bayesian additive regression trees

      Preprint
      , ,

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          We develop a Bayesian "sum-of-trees" 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 model-free variable selection. BART's many features are illustrated with a bake-off against competing methods on 42 different data sets, with a simulation experiment and on a drug discovery classification problem.

          Related collections

          Most cited references13

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Discussion of "Least angle regression" by Efron et al

          (2004)
          Discussion of ``Least angle regression'' by Efron et al. [math.ST/0406456]
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Bayesian Analysis of Binary and Polychotomous Response Data

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Shape Quantization and Recognition with Randomized Trees

                Bookmark

                Author and article information

                Journal
                2008-06-19
                2010-10-07
                Article
                10.1214/09-AOAS285
                0806.3286
                9c70d6f3-d230-4f96-9a9d-ac446fc7ee90

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                IMS-AOAS-AOAS285
                Annals of Applied Statistics 2010, Vol. 4, No. 1, 266-298
                Published in at http://dx.doi.org/10.1214/09-AOAS285 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
                stat.ME stat.AP stat.ML
                vtex

                Applications,Machine learning,Methodology
                Applications, Machine learning, Methodology

                Comments

                Comment on this article