Jaakkola, T.S. and Jordan, M.I. (1999)
"Variational Probabilistic Inference and the QMR-DT Network",
Volume 10, pages 291-322.
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Abstract: We describe a variational approximation method for efficient
inference in large-scale probabilistic models. Variational methods
are deterministic procedures that provide approximations to marginal
and conditional probabilities of interest. They provide alternatives
to approximate inference methods based on stochastic sampling or
search. We describe a variational approach to the problem of
diagnostic inference in the `Quick Medical Reference' (QMR) network.
The QMR network is a large-scale probabilistic graphical model built
on statistical and expert knowledge. Exact probabilistic inference is
infeasible in this model for all but a small set of cases. We
evaluate our variational inference algorithm on a large set of
diagnostic test cases, comparing the algorithm to a state-of-the-art
stochastic sampling method.
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