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.

翻译：变数消除是贝叶斯网络概率推论的基本算法。 在本文中,我们提出了变数消除的新进化方法,在回答推论询问时可以带来显著的效率收益。我们用真实世界的贝叶斯网络评估我们的技术。我们的结果显示,适度的进化可以大大改善查询的运行时间。此外,与也依赖进化的交接树方法相比,我们的方法在推论期间使用显著轻化的进化方法实现了类似的效率。