Practical bayesian network inference

点击翻译按钮获取中文摘要

Bayesian networks are a means of exploiting conditional independence information in a joint probability distribution to facilitate both a more compact representation of the distribution, and faster inference from the 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 diagnosis. 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.