Some practical issues in inference in hybrid Bayesian networks with deterministic conditionals

In this paper we analyze the use of hybrid Bayesian networks in domains that include deterministic conditionals for continuous variables. We show how exact inference can become infeasible even for small networks, due to the difficulty in handling functional relationships. We compare two strategies for carrying out the inference task, using mixtures of polynomials (MOPs) and mixtures of truncated exponentials (MTEs).