Refractor Importance Sampling
  In this paper we introduce Refractor Importance Sampling (RIS), an
improvement to reduce error variance in Bayesian network importance sampling
propagation under evidential reasoning. We prove the existence of a collection
of importance functions that are close to the optimal importance function under
evidential reasoning. Based on this theoretic result we derive the RIS
algorithm. RIS approaches the optimal importance function by applying localized
arc changes to minimize the divergence between the evidence-adjusted importance
function and the optimal importance function. The validity and performance of
RIS is empirically tested with a large setof synthetic Bayesian networks and
two real-world networks.
