论文标题
广义贝叶斯添加剂回归树模型：超越条件性结合
Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy
论文作者
论文摘要
贝叶斯添加剂回归树在近年来由于将机器学习技术与原则上的不确定性定量相结合的能力而引起了人们的兴趣。但是，用于适合巴特模型的贝叶斯背贴算法将其应用限制为有条件结合存在的一小类模型。在本文中，我们通过引入一个非常简单的，无可逆的跳跃马尔可夫链蒙特卡洛算法来大大扩展BART的适用性到任意\ Emph {广义Bart}模型。我们的算法仅要求用户能够计算可能性和（可选）其梯度和Fisher信息。潜在应用非常广泛；我们考虑了生存分析，结构化的异性回归和伽马形状回归中的例子。
Bayesian additive regression trees have seen increased interest in recent years due to their ability to combine machine learning techniques with principled uncertainty quantification. The Bayesian backfitting algorithm used to fit BART models, however, limits their application to a small class of models for which conditional conjugacy exists. In this article, we greatly expand the domain of applicability of BART to arbitrary \emph{generalized BART} models by introducing a very simple, tuning-parameter-free, reversible jump Markov chain Monte Carlo algorithm. Our algorithm requires only that the user be able to compute the likelihood and (optionally) its gradient and Fisher information. The potential applications are very broad; we consider examples in survival analysis, structured heteroskedastic regression, and gamma shape regression.
