论文标题
用于概率编程的贝叶斯添加期回归树
Bayesian additive regression trees for probabilistic programming
论文作者
论文摘要
贝叶斯加性回归树（BART）是一种近似函数的非参数方法。这是一种基于许多树木的总和来定于推理的黑框方法，主要是通过限制树木的学习能力，以便没有单个树能够解释数据，而是树木的总和。我们在概率编程语言（PPL）的背景下讨论BART，即，我们将Bart作为原始性，可以用作概率模型的组成部分，而不是独立模型。具体而言，我们介绍了Python库PYMC-BART，该库是通过扩展PYMC（用于概率编程的库）来起作用的。我们展示了一些可以使用PYMC-BART构建的模型的示例，讨论了选择超参数的建议，最后，我们关闭了实施和将来的改进方向的局限性。
Bayesian additive regression trees (BART) is a non-parametric method to approximate functions. It is a black-box method based on the sum of many trees where priors are used to regularize inference, mainly by restricting trees' learning capacity so that no individual tree is able to explain the data, but rather the sum of trees. We discuss BART in the context of probabilistic programming languages (PPL), i.e., we present BART as a primitive that can be used as a component of a probabilistic model rather than as a standalone model. Specifically, we introduce the Python library PyMC-BART, which works by extending PyMC, a library for probabilistic programming. We showcase a few examples of models that can be built using PyMC-BART, discuss recommendations for the selection of hyperparameters, and finally, we close with limitations of our implementation and future directions for improvement.
