Exploring Localization in Bayesian Networks for Large Expert Systems
  Current Bayesian net representations do not consider structure in the domain
and include all variables in a homogeneous network. At any time, a human
reasoner in a large domain may direct his attention to only one of a number of
natural subdomains, i.e., there is ?localization' of queries and evidence. In
such a case, propagating evidence through a homogeneous network is inefficient
since the entire network has to be updated each time. This paper presents
multiply sectioned Bayesian networks that enable a (localization preserving)
representation of natural subdomains by separate Bayesian subnets. The subnets
are transformed into a set of permanent junction trees such that evidential
reasoning takes place at only one of them at a time. Probabilities obtained are
identical to those that would be obtained from the homogeneous network. We
discuss attention shift to a different junction tree and propagation of
previously acquired evidence. Although the overall system can be large,
computational requirements are governed by the size of only one junction tree.
