Context-Specific Likelihood Weighting
  Sampling is a popular method for approximate inference when exact inference
is impractical. Generally, sampling algorithms do not exploit context-specific
independence (CSI) properties of probability distributions. We introduce
context-specific likelihood weighting (CS-LW), a new sampling methodology,
which besides exploiting the classical conditional independence properties,
also exploits CSI properties. Unlike the standard likelihood weighting, CS-LW
is based on partial assignments of random variables and requires fewer samples
for convergence due to the sampling variance reduction. Furthermore, the speed
of generating samples increases. Our novel notion of contextual assignments
theoretically justifies CS-LW. We empirically show that CS-LW is competitive
with state-of-the-art algorithms for approximate inference in the presence of a
significant amount of CSIs.
