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Variational Approximations in a Broad and DetailedProbabilistic Model for Medical

Exact inference in large, detailed probabilistic models for medical diagnosis is typically computationally infeasible, and approximate schemes are therefore of great importance. In this paper, we consider variational methods, which provide bounds on the probabilities of interest. We sketch some characteristics of a typical broad and detailed probabilistic model (BDPM) for medical diagnosis and describe how recently developed variational techniques can be applied for approximate inference in such a model. Currently we are developing a BDPM to study the practical feasibility and the usefulness of a system based on such a model in medical practice.