Developing runtime structures for Bayesian network inference

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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.