1. A method for training a probabilistic boosting tree, comprising: receiving training data at a graphics processing unit (GPU); dividing the training data into a first dataset and a second dataset using a classifier; training a first sub-tree and a second sub-tree at the GPU, the first sub-tree using the first dataset and the second sub-tree using the second dataset; generating a posterior distribution model based on the trained first sub-tree and the trained second sub-tree.