  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.
