A Standard Approach for Optimizing Belief Network Inference using Query
  DAGs
  This paper proposes a novel, algorithm-independent approach to optimizing
belief network inference. rather than designing optimizations on an algorithm
by algorithm basis, we argue that one should use an unoptimized algorithm to
generate a Q-DAG, a compiled graphical representation of the belief network,
and then optimize the Q-DAG and its evaluator instead. We present a set of
Q-DAG optimizations that supplant optimizations designed for traditional
inference algorithms, including zero compression, network pruning and caching.
We show that our Q-DAG optimizations require time linear in the Q-DAG size, and
significantly simplify the process of designing algorithms for optimizing
belief network inference.
