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 a survey) and causal mechanisms from sample data [H96, P88, S93, P95, F98]. In most settings we should expect the former to be di cult, and the latter potentially impossible without experimental intervention. In this work, we examine some restricted settings in which the ideal can be obtained: e cient algorithms that perfectly reconstruct the hidden structure solely on the basis of observed sample data.