Sensitivity analysis of prior beliefs in advanced Bayesian networks

Longxue He, Michael Beer, Matteo Broggi, Pengfei Wei, Antonio Topa Gomes

科研成果: 书/报告/会议事项章节会议稿件同行评审

2 引用 (Scopus)

摘要

Bayesian Network (BN) is an efficient model tool for approximate reasoning based on machine learning. It has been widely used for supporting the decision in many engineering applications such as geotechnical engineering. However, the current studies on BN are mostly on uncertainty quantification and decision-making, while the sensitivity analysis on BN, which may provide much more insights for decision-making, has not received much attention. The current research on sensitivity analysis of BN mainly focuses on local method, and there is a need to develop global sensitivity analysis (GSA) for both forward and backward inferences of BN. We present in this paper GSA analysis for BN within two different settings. For the first setting, it is assumed that the BN nodes, as well as their connection are characterized by precise (conditional) probabilities, and we introduce GSA for both forward and backward analysis. It is shown that, by forward analysis, the GSA indices can effectively identify the nodes which make the most contribution to the end nodes directly related to the reliability; by backward analysis, the GSA indices can inform the most important information needs to be collected for BN model updating. The second setting concerns the incomplete knowledge of nodes and their connections, and it is assumed these quantities are characterized by imprecise probability models. In this setting, the GSA is then introduced, and implemented with the newly developed non-intrusive imprecise stochastic simulation (NISS) method, for learning the most important epistemic uncertainty sources, by reducing which the robustness of the BN inference can be enhanced the most. The above theoretical developments are then applied to an infinite slope reliability analysis problem.

源语言英语
主期刊名2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
出版商Institute of Electrical and Electronics Engineers Inc.
776-783
页数8
ISBN(电子版)9781728124858
DOI
出版状态已出版 - 12月 2019
活动2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 - Xiamen, 中国
期限: 6 12月 20199 12月 2019

出版系列

姓名2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

会议

会议2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
国家/地区中国
Xiamen
时期6/12/199/12/19

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