Sequentially Fitting ``Inclusive'' Trees for Inference in Noisy-OR Networks

Forexample, in medical diagnosis, the presence of a symptom can be expressed as a noisy-OR of the diseases that may cause the symptom - on some occasions, a disease may fail to activate the symptom. Inference in richly-connected noisy-OR networks is intractable, butapproximate methods (e .g., variational techniques) are showing increasing promise as practical solutions. One problem withmost approximations is that they tend to concentrate on a relatively small number of modes in the true posterior, ignoring otherplausible configurations of the hidden variables.