Abstract Discrete Bayesian Belief Network (BBN) has become a popular method for the
analysis of complex systems in various domains of application. One of its pillar is the …

Damage caused by third-party excavation is one of the leading threats to the structural
integrity of underground energy pipelines. Based on fault tree models reported in the …

In 2022, the US EPA published an important risk assessment concluding that “Compared to
the current annual standard, meeting a revised annual standard with a lower level is …

In the context of Industry 4.0, amidst the normalization of multi-variety and low-volume
custom flexible production models, aircraft mixed-model assembly lines (AMMALs) have …

The present study integrates the corrosion growth modeling, reliability analysis and
quantification of measurement errors of in-line inspection (ILI) tools in a single dynamic …

Node order is one of the most important factors in learning the structure of a Bayesian
network (BN) for probabilistic reasoning. To improve the BN structure learning, we propose a …

Large-scale management, protection, and restoration of wetlands require knowledge of their
hydrology, ie, the status and dynamics of the groundwater table, which determine the …

This paper develops a safe control scheme under the abnormity based on Bayesian network
(BN) for the thickening process of gold hydrometallurgy. By analyzing the causes and …

叶斯网络参数. 定性最大后验估计(QMAP) 方法是目前小数据集条件下贝叶斯网络参数学习精度
最高的算法. 然而, 当参数约束数量较多或参数可行域较小时, QMAP 算法中的拒绝 …

Learning parameters of a probabilistic model is a necessary step in machine learning tasks.
We present a method to improve learning from small datasets by using monotonicity …