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Journal of Artificial Intelligence Research 10 (1999), pp. 291-322. Submitted 10/98; published 5/99.
© 1999 AI Access Foundation and Morgan Kaufmann Publishers. All rights reserved.

Variational Probabilistic Inference
and the QMR-DT Network

Tommi S. Jaakkola
Artificial Intelligence Laboratory,
Massachusetts Institute of Technology,
Cambridge, MA 02139 USA

Michael I. Jordan
Computer Science Division and Department of Statistics,
University of California,
Berkeley, CA 94720-1776 USA


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.

Michael Jordan
Sun May 9 16:22:01 PDT 1999