Variational methods and the QMR-DT database

We describe a variational approximation method for efficient probabilistic inference in dense graphical models. Variational methods are deterministic approximation procedures that provide bounds on probabilities of interest. They provide alternatives to approximate inference methods based on stochastic sampling or search. We exemplify variational methods by an application to the problem of diagnostic inference in the QMR-DT database. The QMR-DT database is a large-scale graphical model based on statistical and expert knowledge in internal medicine. The size and complexity of the database render exact probabilistic diagnosis infeasible for all but a small set of cases. We describe a variational inference algorithm for the QMR-DT database, evaluate the accuracy of our algorithm on a set of standard diagnostic cases and compare to stochastic sampling methods.