论文标题
动态不确定因果关系图的一种新的推理算法，基于条件抽样方法的复杂情况
A New Inference algorithm of Dynamic Uncertain Causality Graph based on Conditional Sampling Method for Complex Cases
论文作者
论文摘要
动态不确定因果图（DUCG）是一个最近提出的用于诊断复杂系统的模型。它对核电厂，化学系统和航天器等行业系统的性能很好。但是，在某些情况下，可变状态组合爆炸仍然是一个问题，可能导致DUCG推断的效率低下甚至残疾。在临床诊断的情况下，当许多中间原因是未知的，而下游结果在DUCG图中是知道的，则在推理计算过程中可能会出现组合爆炸。蒙特卡洛采样是解决此问题的典型算法。但是，我们面临的情况是，案件的发生率很小，例如$ 10^{ - 20} $，这意味着需要大量的采样。本文提出了一种基于条件随机模拟的新方案，该方案是从采样回路中条件概率的预期而不是计数采样频率的最终结果，从而克服了问题。结果，所提出的算法所需的时间要比前面提出的DUCG递归推理算法要少得多。此外，给出了基于设计示例的融合率的简单分析，以显示提出的方法的优势。 ％此外，本文还介绍了DUCG中存在的逻辑门，逻辑周期和并行化的支持。新算法减少了时间的消耗量，并且在病毒性乙型肝炎的实用图中的误差率为2.7％。
Dynamic Uncertain Causality Graph(DUCG) is a recently proposed model for diagnoses of complex systems. It performs well for industry system such as nuclear power plants, chemical system and spacecrafts. However, the variable state combination explosion in some cases is still a problem that may result in inefficiency or even disability in DUCG inference. In the situation of clinical diagnoses, when a lot of intermediate causes are unknown while the downstream results are known in a DUCG graph, the combination explosion may appear during the inference computation. Monte Carlo sampling is a typical algorithm to solve this problem. However, we are facing the case that the occurrence rate of the case is very small, e.g. $10^{-20}$, which means a huge number of samplings are needed. This paper proposes a new scheme based on conditional stochastic simulation which obtains the final result from the expectation of the conditional probability in sampling loops instead of counting the sampling frequency, and thus overcomes the problem. As a result, the proposed algorithm requires much less time than the DUCG recursive inference algorithm presented earlier. Moreover, a simple analysis of convergence rate based on a designed example is given to show the advantage of the proposed method. % In addition, supports for logic gate, logic cycles, and parallelization, which exist in DUCG, are also addressed in this paper. The new algorithm reduces the time consumption a lot and performs 3 times faster than old one with 2.7% error ratio in a practical graph for Viral Hepatitis B.
