IEEE Trans Neural Netw Learn Syst. 2018 May;29(5):1637-1651. doi: 10.1109/TNNLS.2017.2673243. Epub 2017 Mar 17.
The dynamic uncertain causality graph (DUCG) is a newly presented framework for uncertain causality representation and probabilistic reasoning. It has been successfully applied to online fault diagnoses of large, complex industrial systems, and decease diagnoses. This paper extends the DUCG to model more complex cases than what could be previously modeled, e.g., the case in which statistical data are in different groups with or without overlap, and some domain knowledge and actions (new variables with uncertain causalities) are introduced. In other words, this paper proposes to use -mode, -mode, and -mode of the DUCG to model such complex cases and then transform them into either the standard -mode or the standard -mode. In the former situation, if no directed cyclic graph is involved, the transformed result is simply a Bayesian network (BN), and existing inference methods for BNs can be applied. In the latter situation, an inference method based on the DUCG is proposed. Examples are provided to illustrate the methodology.
动态不确定因果图 (DUCG) 是一种新提出的不确定因果表示和概率推理框架。它已成功应用于大型复杂工业系统的在线故障诊断和疾病诊断。本文将 DUCG 扩展到可以建模比以前更复杂的情况，例如，统计数据分属于不同组，或者存在重叠，以及引入一些领域知识和操作（具有不确定因果关系的新变量）的情况。换句话说，本文提出使用 DUCG 的 -mode、-mode 和 -mode 来建模这种复杂情况，然后将其转换为标准的 -mode 或标准的 -mode。在前一种情况下，如果不涉及有向循环图，则转换结果只是一个贝叶斯网络 (BN)，可以应用现有的 BN 推理方法。在后一种情况下，提出了一种基于 DUCG 的推理方法。提供了示例来说明该方法。
