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Abstract:
Layered Sigmoid Belief Networks are directed graphical models
in which the local conditional probabilities are parameterised by
weighted sums of parental states. Learning and inference in such
networks are generally intractable, and approximations need to be
considered. Progress in learning these networks has been made by
using variational procedures. We demonstrate, however, that
variational procedures can be inappropriate for the equally
important issue of inference - that is, calculating marginals of
the network. We introduce an alternative procedure, based on
assuming that the weighted input to a node is approximately
Gaussian distributed. Our approach goes beyond previous Gaussian
field assumptions in that we take into account correlations between
parents of nodes. This procedure is specialized for calculating
marginals and is significantly faster and simpler than the
variational procedure.
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