A causal network is frequently used as a representation for qualitative medical knowledge,
in which conditional probability tables on appropriate sets of variables form the quantitative part of the accumulated experience. For probabilities temporarily assumed known, we describe efficient algorithms for propagating the effects of multiple items of evidence around multiply-connected networks and hence providing precise probabilistic revision of beliefs concerning the current patient. As a database accumulates we also require the quantitative aspects of the model to be updated, as well as to learn about the qualitative structure, and we suggest some formal statistical tools for these problems.