Tractable Inference in Hybrid Bayesian Networks with Deterministic Conditionals using Re-approximations
Rafael Rum, Prakash Pundalik Shenoy · 2012
In this paper we study the problem of inference in hybrid Bayesian networks containing deterministic conditionals. The diculties in handling deterministic conditionals for continuous variables can make inference intractable even for small networks. We describe the use of re-approximations to reduce the complexity of the potentials that arise in the intermediate steps of the inference process. We show how the idea behind re-approximations can be applied to the frameworks of mixtures of polynomials and mixtures of truncated exponentials. Finally, we illustrate our approach by solving a small stochastic PERT network modeled as an hybrid Bayesian network.