Defines what it means to compile a Bayesian network together with its evidence, and shows the payoff in maximum-likelihood estimation, sensitivity analysis and MAP, with results from genetic linkage analysis.
The paper defines the idea of compiling a Bayesian network with its evidence already folded in, and gives a practical way to do it using logical processing. The approach pays off in several settings, maximum-likelihood estimation, sensitivity analysis and MAP computations among them, and the paper gives empirical results from genetic linkage analysis. It also applies to networks without determinism, and empirically subsumes the performance of the quickscore algorithm on noisy-or networks. The 2008 journal paper names it as one of the three papers it is based on.
Diagnosis is reasoning under evidence. Compiling with the evidence in hand is one of the reasons the method answers quickly once the model exists.
A methods paper. No product claims attach to it.