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Abstract:
We present a class of approximate inference algorithms for
two-layer graphical models of the QMR-DT type. We give convergence
rates for these algorithms as the networks become large (subject to
conditions on the size of the weights that ensure local averaging
behavior), and verify these theoretical predictions empirically. We
also present empirical results on the QMR-DT diagnostic inference
problem.
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