校准动态模拟的离散边界条件:一个组合的近似贝叶斯计算序列蒙特卡洛 (ABC-SMC) 方法.
Jah Shamas1, Tim Rogers1, Anton Krynkin1
1Department of Mechanical Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
一个新的组合近似贝叶斯计算序列蒙特卡洛 (ABC-SMC) 算法估计了高维二进制参数. 这种方法增强了结构动态中的不确定性量化,对复杂系统有效.
科学领域:
- 计算统计学 计算统计学
- 贝叶斯的推理是贝叶斯的推理.
- 机器学习 机器学习
背景情况:
- 当概率函数难以处理时,用于参数估计的近似贝叶斯计算序列蒙特卡洛 (ABC-SMC).
- 传统的ABC-SMC方法主要设计用于连续参数.
- 在许多科学领域,高维二进制参数推断仍然是一个挑战.
研究的目的:
- 引入ABC-SMC的新改编,称为组合式ABC-SMC,用于推断高维二进制参数.
- 解决现有方法在处理不连续的复杂参数空间方面的局限性.
- 在实际的结构动力学应用中证明拟议方法的有效性.
主要方法:
- 该研究通过修改提案分布以针对高维二进制变量来调整传统的ABC-SMC算法.
- 一个模拟模型取代了难以处理的概率函数,生成用于推断的人工数据.
- 组合式ABC-SMC方案通过连续的采样"波"来代地改进参数估计.
主要成果:
- 组合式ABC-SMC方法在模拟光纤传感器结构动态实验中成功推断了不确定的边界条件.
- 使用多个振动数据集验证了算法的性能,证实了它的稳定性.
- 对不同度量函数的比较分析突出了它们对算法的趋同的影响.
结论:
- 拟议的组合式ABC-SMC算法为高维二进制空间中的参数估计提供了强大的解决方案.
- 这种新的方法提高了不确定性量化能力,特别是在结构动态等领域.
- 该方法的适应性和验证的有效性使其成为复杂的推理问题的有价值的工具,在这些问题中,概率是不可用的.
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