  Variable Elimination is a fundamental algorithm for probabilistic inference
over Bayesian networks. In this paper, we propose a novel materialization
method for Variable Elimination, which can lead to significant efficiency gains
when answering inference queries. We evaluate our technique using real-world
Bayesian networks. Our results show that a modest amount of materialization can
lead to significant improvements in the running time of queries. Furthermore,
in comparison with junction tree methods that also rely on materialization, our
approach achieves comparable efficiency during inference using significantly
lighter materialization.
