Laplace Propagation

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

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Authors

Eleazar Eskin, Alex Smola, S.v.n. Vishwanathan

Abstract

We present a novel method for approximate inference in Bayesian mod- els and regularized risk functionals. It is based on the propagation of mean and variance derived from the Laplace approximation of condi- tional probabilities in factorizing distributions, much akin to Minka’s Expectation Propagation. In the jointly normal case, it coincides with the latter and belief propagation, whereas in the general case, it provides an optimization strategy containing Support Vector chunking, the Bayes Committee Machine, and Gaussian Process chunking as special cases.