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Bayesian Additive Regression Trees: A Review and Look Forward
Annual Review of Statistics and Its Application ( IF 7.9 ) Pub Date : 2020-03-09 , DOI: 10.1146/annurev-statistics-031219-041110
Jennifer Hill 1 , Antonio Linero 2 , Jared Murray 3
Affiliation  

Bayesian additive regression trees (BART) provides a flexible approach to fitting a variety of regression models while avoiding strong parametric assumptions. The sum-of-trees model is embedded in a Bayesian inferential framework to support uncertainty quantification and provide a principled approach to regularization through prior specification. This article presents the basic approach and discusses further development of the original algorithm that supports a variety of data structures and assumptions. We describe augmentations of the prior specification to accommodate higher dimensional data and smoother functions. Recent theoretical developments provide justifications for the performance observed in simulations and other settings. Use of BART in causal inference provides an additional avenue for extensions and applications. We discuss software options as well as challenges and future directions.

中文翻译:


贝叶斯加性回归树:回顾与展望

贝叶斯加性回归树(BART)提供了一种灵活的方法来拟合各种回归模型,同时避免了强大的参数假设。树和模型被嵌入到贝叶斯推理框架中以支持不确定性量化,并提供一种通过先验规范进行正则化的原则方法。本文介绍了基本方法,并讨论了支持各种数据结构和假设的原始算法的进一步开发。我们描述了现有技术规范的扩充,以适应更高维度的数据和更平滑的功能。最近的理论发展为模拟和其他设置中观察到的性能提供了依据。在因果推断中使用BART为扩展和应用程序提供了其他途径。

更新日期:2020-03-09
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