Solving Hybrid Influence Diagrams with Deterministic Variables
  We describe a framework and an algorithm for solving hybrid influence
diagrams with discrete, continuous, and deterministic chance variables, and
discrete and continuous decision variables. A continuous chance variable in an
influence diagram is said to be deterministic if its conditional distributions
have zero variances. The solution algorithm is an extension of Shenoy's fusion
algorithm for discrete influence diagrams. We describe an extended
Shenoy-Shafer architecture for propagation of discrete, continuous, and utility
potentials in hybrid influence diagrams that include deterministic chance
variables. The algorithm and framework are illustrated by solving two small
examples.
