一个谨慎的用户指南,用于将HMM应用于物理系统
M Schweiger1,2, A Saurabh1,2, S Pressé1,2,3
1Department of Physics, Arizona State University, Tempe, Arizona 85281, USA.
The Journal of chemical physics
|December 2, 2025
概括
应用到连续物理系统的隐藏马尔科夫模型 (HMM) 可能会产生误导性的结果. 推断的状态往往反映了测量选择,而不是系统.
科学领域:
- 物理 物理学 物理
- 复杂的系统复杂的系统.
- 数据分析 数据分析
背景情况:
- 连续的物理系统在空间和时间中进化.
- 隐藏的马尔科夫模型 (HMM) 被广泛用于时间序列分析.
- HMM最初是为离散的时间和空间而开发的.
研究的目的:
- 研究将离散HMM应用于连续系统的含义.
- 确定HMM为物理数据提供可解释结果的条件.
- 检查测量协议对HMM推理的影响.
主要方法:
- 使用Langevin动态在有效潜力中生成合成数据.
- 用标准HMM分析时间序列数据.
- 探索数据采集方案的变化.
主要成果:
- 离散状态HMM充当抽象,推断状态反映模型选择.
- 测量协议可以显著影响和"调整"恢复的HMM状态.
- 误导性中间状态可以被复制性地恢复,即使对于简单的潜能.
结论:
- 物理建模中的HMM需要意识到由于离散近似而存在的局限性.
- 对连续的空间和时间的概括很重要.
- 准确的测量噪声建模对于可靠的HMM应用至关重要.
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