论文信息 - Approximate Inference A lgorithms for Two-Layer Bayesian Networks

Approximate Inference A lgorithms for Two-Layer Bayesian Networks

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

Michael I. Jordan | Andrew Y. Ng | A. Ng

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