Localized Partial Evaluation of Belief Networks
  Most algorithms for propagating evidence through belief networks have been
exact and exhaustive: they produce an exact (point-valued) marginal probability
for every node in the network. Often, however, an application will not need
information about every n ode in the network nor will it need exact
probabilities. We present the localized partial evaluation (LPE) propagation
algorithm, which computes interval bounds on the marginal probability of a
specified query node by examining a subset of the nodes in the entire network.
Conceptually, LPE ignores parts of the network that are "too far away" from the
queried node to have much impact on its value. LPE has the "anytime" property
of being able to produce better solutions (tighter intervals) given more time
to consider more of the network.
