In this paper, we present and compare a wide variety of tree-based algorithms for learning and evaluating conditional density estimates over continuous variables. These trees can be thought of as discretizations that vary ac cording to the particular interactions being modeled; however, the density within a given leaf of the tree need not be ass umed con stant, and we show that such nonuniform leaf densities lead to more accurate density esti mation. We have developed Bayesian net work structure-learning algorithms that em ploy these tree-based conditional density rep resentations, and we show that they can be used to practically learn complex joint prob ability models over dozens of continuous vari ables from thousands of data points. We focus on fi nding models that are simultaneously ac curate, fast to learn, and fast to evaluate once they are learned.
Finally, a set of tree models for conditional density estimation are surveyed in REF .
Interpolating Conditional Density Trees
UAI 2002
arXiv: Learning
Computer Science Mathematics