Exploiting Evidence in Probabilistic Inference
  We define the notion of compiling a Bayesian network with evidence and
provide a specific approach for evidence-based compilation, which makes use of
logical processing. The approach is practical and advantageous in a number of
application areas-including maximum likelihood estimation, sensitivity
analysis, and MAP computations-and we provide specific empirical results in the
domain of genetic linkage analysis. We also show that the approach is
applicable for networks that do not contain determinism, and show that it
empirically subsumes the performance of the quickscore algorithm when applied
to noisy-or networks.
