从不完整的数据中进行量化系统风险评估,使用信念网络和对比比较诱导
Cristina De Persis1, José Luis Bosque2, Irene Huertas3
1ATG-Europe for ESA, Noordwijk, The Netherlands.
概括
本研究提出了贝叶斯的风险评估方法,使用断层树和信念网络进行风险评估. 它简化了提取和量化不确定性,即使有有限的数据,更好地进行风险分析.
科学领域:
- 风险评估 风险评估
- 贝叶斯的推理是贝叶斯的推理.
- 信念网络是一种信仰网络.
背景情况:
- 传统的风险评估通常在有限的数据下扎.
- 断层树分析是一种用于建模风险过程的常用方法.
- 量化不确定性对于可靠的风险评估至关重要.
研究的目的:
- 开发使用断层树进行风险评估的贝叶斯方法.
- 为了应对贝叶斯诱导中有限数据的挑战.
- 提供一个框架,用观察数据来评估后期概率.
主要方法:
- 建模风险过程作为断层树和信念网络.
- 使用对对比方法引出先前的概率.
- 实现一个完全贝叶斯更新程序后期概率.
主要成果:
- 展示了在风险评估中处理有限数据的方法.
- 展示了数据观察和信息收益之间的权衡分析.
- 成功地将该方法应用于三个现实世界的例子,包括航天器再入风险.
结论:
- 建议的贝叶斯方法有效量化了风险评估中的不确定性,而数据有限.
- 这种方法简化了提取过程,同时保持了分析的严谨性.
- 该方法为数据稀缺环境中的风险分析提供了强大的框架.
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