Inference in Multiply Sectioned Bayesian Networks with Extended
  Shafer-Shenoy and Lazy Propagation
  As Bayesian networks are applied to larger and more complex problem domains,
search for flexible modeling and more efficient inference methods is an ongoing
effort. Multiply sectioned Bayesian networks (MSBNs) extend the HUGIN inference
for Bayesian networks into a coherent framework for flexible modeling and
distributed inference.Lazy propagation extends the Shafer-Shenoy and HUGIN
inference methods with reduced space complexity. We apply the Shafer-Shenoy and
lazy propagation to inference in MSBNs. The combination of the MSBN framework
and lazy propagation provides a better framework for modeling and inference in
very large domains. It retains the modeling flexibility of MSBNs and reduces
the runtime space complexity, allowing exact inference in much larger domains
given the same computational resources.
