Environmental Health Sciences

Learning a distribution conditional on a set of discrete-valued features is a commonly en-countered task. This becomes more challeng-ing with a high-dimensional feature set when there is the possibility of interaction between the features. In addition, many frequently applied techniques consider only prediction of the mean, but the complete conditional density is needed to answer more complex questions. We demonstrate a novel nonpara-metric Bayes method based upon a tensor factorization of feature-dependent weights for Gaussian kernels. The method makes use of multistage feature selection for dimension re-duction. The resulting conditional density morphs flexibly with the selected features.