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Abstract:
The Cluster Variation method is a class of approximation
methods containing the Bethe and Kikuchi approximations as
special cases. We derive two novel iteration schemes for the
Cluster Variation Method. One is a fixed point iteration scheme
which gives a significant improvement over loopy BP, mean field
and TAP methods on directed graphical models. The other is a
gradient based method, that is guaranteed to converge and is
shown to give useful results on random graphs with mild
frustration. We conclude that the methods are of significant
practical value for large inference problems.
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