Approximate Inference Algorithms for Hybrid Bayesian Networks with
  Discrete Constraints
  In this paper, we consider Hybrid Mixed Networks (HMN) which are Hybrid
Bayesian Networks that allow discrete deterministic information to be modeled
explicitly in the form of constraints. We present two approximate inference
algorithms for HMNs that integrate and adjust well known algorithmic principles
such as Generalized Belief Propagation, Rao-Blackwellised Importance Sampling
and Constraint Propagation to address the complexity of modeling and reasoning
in HMNs. We demonstrate the performance of our approximate inference algorithms
on randomly generated HMNs.
