IEEE Trans Neural Netw Learn Syst. 2014 Apr;25(4):645-63. doi: 10.1109/TNNLS.2013.2279320.
Graphical models for probabilistic reasoning are now in widespread use. Many approaches have been developed such as Bayesian network. A newly developed approach named as dynamic uncertain causality graph (DUCG) is initially presented in a previous paper, in which only the inference algorithm in terms of individual events and probabilities is addressed. In this paper, we first explain the statistic basis of DUCG. Then, we extend the algorithm to the form of matrices of events and probabilities. It is revealed that the representation of DUCG can be incomplete and the exact probabilistic inference may still be made. A real application of DUCG for fault diagnoses of a generator system of a nuclear power plant is demonstrated, which involves > 600 variables. Most inferences take < 1 s with a laptop computer. The causal logic between inference result and observations is graphically displayed to users so that they know not only the result, but also why the result obtained.
概率推理的图形模型现在得到了广泛的应用。已经开发了许多方法，例如贝叶斯网络。一种名为动态不确定因果图 (DUCG) 的新方法在之前的一篇论文中首次提出，其中仅涉及单个事件和概率的推理算法。在本文中，我们首先解释了 DUC 的统计基础。然后，我们将算法扩展到事件和概率的矩阵形式。结果表明，DUC 的表示可能不完整，但仍可以进行精确的概率推理。通过一个涉及 > 600 个变量的核电厂发电机系统故障诊断的实际应用，证明了 DUC 的应用。大多数推断在笔记本电脑上用时不到 1 秒。推理结果和观测之间的因果逻辑以图形方式显示给用户，以便他们不仅知道结果，还知道为什么得到这个结果。
