Brendan J. Frey, Neil Lawrence and Christopher M. Bishop 1998.
Markovian inference in belief networks.
Submitted for presentation at the
Neural Information Processing Systems Conference, Denver, Colorado,
Dec. 1998.
Bayesian belief networks can represent the
complicated probabilistic processes that form natural sensory inputs.
Once the parameters of the network have been learned,
nonlinear inferences about the input can be made by computing
the posterior distribution over the hidden units
(e.g., depth in stereo vision) given the input.
Computing the posterior distribution exactly is not practical in
richly-connected networks, but
it turns out that by using a variational (a.k.a., mean field) method,
it is easy to find a product-form distribution that approximates
the true posterior distribution. This approximation assumes that the
hidden variables are independent given the current input. In this paper,
we explore a more powerful variational technique that models the
posterior distribution using a Markov chain. We compare this method
with inference using mean fields and mixtures of mean fields in
randomly generated networks.
Compressed postscript,
uncompressed postscript.
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