Developing runtime structures for Bayesian network inference

贝叶斯网络是一种利用联合概率分布中的条件独立性信息来促进联合分布的紧凑表示和从该分布更快地推断的方法，而不牺牲原始信息的完整性。这对于变量以复杂方式相互作用的环境中的概率决策至关重要，例如医疗决策。它还有助于并提高分类、预测和故障诊断的准确性。任何在不牺牲准确性的情况下提高决策效率的软件，都会使在消费者硬件上的部署变得更可行。

Bayesian networks are a means of exploiting conditional independence information in joint probability distributions to facilitate both a compact representation of a joint distribution, and faster inferences from that distribution, without sacrificing the integrity of the original information. This is crucial for probabilistic decision-making in settings where variables interact in complex ways, such as medical decision making. It also facilitates and improves the accuracy of classification, forecasting, and fault diagnosis. Any software that improves the efficiency of decision-making, without sacrificing accuracy, makes deployment on consumer hardware more feasible.