On the Concentration of Expectation and Approximate Inference in Layered Networks

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

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XuanLong Nguyen, Michael Jordan


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.