Practical bayesian network inference

实用贝叶斯网络推理

• 批准号: 356052-2008
• 负责人: Grant, Kevin
• 金额: $ 1.09万
• 依托单位: University of Lethbridge
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=483021
• 关键词: Practical; bayesian; network; inference

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.

贝叶斯网络是一种利用联合概率分布中的条件独立信息的手段，以促进分布的更紧凑表示和从分布的更快推断，而不牺牲原始信息的完整性。这对于在变量以复杂方式相互作用的环境中进行概率决策至关重要，例如医疗诊断。 它还有助于提高分类、预测和故障诊断的准确性。任何提高决策效率而不牺牲准确性的软件都使在消费者硬件上的部署更加可行。