Hybrid Bayesian Networks with Linear Deterministic Variables
  When a hybrid Bayesian network has conditionally deterministic variables with
continuous parents, the joint density function for the continuous variables
does not exist. Conditional linear Gaussian distributions can handle such cases
when the continuous variables have a multi-variate normal distribution and the
discrete variables do not have continuous parents. In this paper, operations
required for performing inference with conditionally deterministic variables in
hybrid Bayesian networks are developed. These methods allow inference in
networks with deterministic variables where continuous variables may be
non-Gaussian, and their density functions can be approximated by mixtures of
truncated exponentials. There are no constraints on the placement of continuous
and discrete nodes in the network.
