Compact approximations to Bayesian predictive distributions
We provide a general framework for learning precise, compact, and fast representations of the Bayesian predictive distribution for a model. This framework is based on minimizing the KL divergence between the true predictive density and a suitable compact approximation. We consider various methods for doing this, both sampling based approximations, and deterministic approximations such as expectation propagation. These methods are tested on a mixture of Gaussians model for density estimation and on binary linear classification, with both synthetic data sets for visualization and several real data sets. Our results show significant reductions in prediction time and memory footprint.