JAIR article, "Variational Probabilistic Inference..."

[Steve Minton]

JAIR is pleased to announce the publication of the following article,
which may be of interest to readers of this mailing list:

Jaakkola, T.S. and Jordan, M.I. (1999)
  "Variational Probabilistic Inference and the QMR-DT Network", 
   Volume 10, pages 291-322.

   Available in HTML, PDF, PostScript and compressed PostScript.
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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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