Why is Compiling Lifted Inference into a Low-Level Language so
  Effective?
  First-order knowledge compilation techniques have proven efficient for lifted
inference. They compile a relational probability model into a target circuit on
which many inference queries can be answered efficiently. Early methods used
data structures as their target circuit. In our KR-2016 paper, we showed that
compiling to a low-level program instead of a data structure offers orders of
magnitude speedup, resulting in the state-of-the-art lifted inference
technique. In this paper, we conduct experiments to address two questions
regarding our KR-2016 results: 1- does the speedup come from more efficient
compilation or more efficient reasoning with the target circuit?, and 2- why
are low-level programs more efficient target circuits than data structures?
