Approximate Inference A lgorithms for Two-Layer Bayesian Networks

Part of Advances in Neural Information Processing Systems 12 (NIPS 1999)

Authors

Andrew Ng, Michael Jordan

Abstract

We present a class of approximate inference algorithms for graphical models of the QMR-DT type. We give convergence rates for these al(cid:173) gorithms and for the Jaakkola and Jordan (1999) algorithm, and verify these theoretical predictions empirically. We also present empirical re(cid:173) sults on the difficult QMR-DT network problem, obtaining performance of the new algorithms roughly comparable to the Jaakkola and Jordan algorithm.