论文标题
GP-BART：一种新颖的贝叶斯添加剂回归树使用高斯工艺接近
GP-BART: a novel Bayesian additive regression trees approach using Gaussian processes
论文作者
论文摘要
贝叶斯添加剂回归树（BART）模型是一种集合方法，由于其始终强大的预测性能及其量化不确定性的能力，因此广泛而成功地用于回归任务。 Bart通过一组收缩先验结合了“弱”树模型，每棵树都解释了数据中可变性的一小部分。但是，在需要这样的假设时，缺乏平稳性和缺乏明确的协方差结构会产生较差的性能。高斯工艺贝叶斯添加剂回归树（GP-BART）模型是BART的扩展，它通过假设高斯工艺（GP）先验来解决此限制，以预测所有树木中每个终端节点的预测。该模型的有效性通过应用于模拟和现实世界数据的应用，超过了各种情况下传统建模方法的性能。
The Bayesian additive regression trees (BART) model is an ensemble method extensively and successfully used in regression tasks due to its consistently strong predictive performance and its ability to quantify uncertainty. BART combines "weak" tree models through a set of shrinkage priors, whereby each tree explains a small portion of the variability in the data. However, the lack of smoothness and the absence of an explicit covariance structure over the observations in standard BART can yield poor performance in cases where such assumptions would be necessary. The Gaussian processes Bayesian additive regression trees (GP-BART) model is an extension of BART which addresses this limitation by assuming Gaussian process (GP) priors for the predictions of each terminal node among all trees. The model's effectiveness is demonstrated through applications to simulated and real-world data, surpassing the performance of traditional modeling approaches in various scenarios.
