  Bayesian Decision Trees are known for their probabilistic interpretability.
However, their construction can sometimes be costly. In this article we present
a general Bayesian Decision Tree algorithm applicable to both regression and
classification problems. The algorithm does not apply Markov Chain Monte Carlo
and does not require a pruning step. While it is possible to construct a
weighted probability tree space we find that one particular tree, the
greedy-modal tree (GMT), explains most of the information contained in the
numerical examples. This approach seems to perform similarly to Random Forests.
