Bayes additive regression trees(BART) is a nonparametric regression model which has gained wide -spread popularity in recent years due to its flexibility and high accuracy of estimation .In spatio-temporal related model,the spatio or temporal variables are playing an important role in the model.The BART models select variables with uniform prior distribution that means treat every variable equally.Applying the BART model directly without properly using these prior information is not appropriate.This paper is aimed at a modification to the BART by fixing part of the tree's structure.We call this model partially fixed BART.By this new model we can improve efficiency of estimation.When we don't know the prior information,we can still use the new model to get more accurate estimation and more structure information for future use.Data experiments and real data examples show the improvement comparing to the original Bart model.

翻译：Bayes 添加回归树(BART)是一个非参数回归模型,近年来由于其灵活性和高精确度估算,这一模型广受欢迎。 在spatio-时空相关模型中,spatio 或时间变量在模型中发挥着重要的作用。 BART模型选择了具有统一先前分布的变量,这意味着对每个变量一视同仁。 直接应用 BART 模型而不正确使用这些先前的信息是不合适的。 本文旨在通过确定树的结构部分来修改 BART 。 我们称这个模型为部分固定的 BART.BART。 我们用这个新模型可以提高估算效率。 当我们不知道先前的信息时, 我们还可以使用新模型来获取更准确的估算和更多的结构信息,供未来使用。 Data 实验和真实数据实例显示与原始的Bart模型相比的改进。