Exact Inference of Hidden Structure from Sample Data in Noisy-OR
  Networks
  In the literature on graphical models, there has been increased attention
paid to the problems of learning hidden structure (see Heckerman [H96] for
survey) and causal mechanisms from sample data [H96, P88, S93, P95, F98]. In
most settings we should expect the former to be difficult, and the latter
potentially impossible without experimental intervention. In this work, we
examine some restricted settings in which perfectly reconstruct the hidden
structure solely on the basis of observed sample data.
