On the Concentration of Expectation and Approximate Inference in Layered Networks

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

Authors

XuanLong Nguyen, Michael Jordan

Abstract

We present an analysis of concentration-of-expectation phenomena in layered Bayesian networks that use generalized linear models as the local conditional probabilities. This framework encompasses a wide variety of probability distributions, including both discrete and continuous random variables. We utilize ideas from large deviation analysis and the delta method to devise and evaluate a class of approximate inference algo- rithms for layered Bayesian networks that have superior asymptotic error bounds and very fast computation time.