从宏观数量对联振器模型中的合强度和异质性的贝叶斯估计
Yusuke Kato1, Shuhei Kashiwamura2, Emiri Watanabe3
1The University of Tokyo, Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan.
Physical review. E
|October 21, 2025
概括
本研究引入了贝叶斯框架,用于使用宏观的库拉莫托顺序参数数据估计合振荡器模型参数. 该方法有效地从集体系统行为中估计参数,而不是单个单元.
科学领域:
- 复杂的系统复杂的系统.
- 非线性动力学是一种非线性动力学.
- 统计物理 统计物理
背景情况:
- 宏观振荡源于同步合振荡器,如心跳和火虫闪.
- 现有的参数估计方法通常依赖于单个振荡器数据,而不是宏观量.
- 了解同步机制需要准确的模型参数估计.
研究的目的:
- 提出一个新的贝叶斯框架,用于对联振荡器模型中的参数估计.
- 为了利用宏观时间序列数据,特别是库拉莫托顺序参数,进行估计.
- 开发一种有效的方法来估计后面分布和边际概率.
主要方法:
- 贝叶斯推理框架.贝叶斯推理框架.
- 交换蒙特卡罗方法,以获得高效的后向分布和边际概率估计.
- 使用库拉莫托订单参数的时间序列数据.
主要成果:
- 成功开发并验证了参数估计的贝叶斯框架.
- 证明了框架仅使用宏观数据来估计参数的能力.
- 分析了观测噪声和系统大小对估计准确性的影响.
结论:
- 建议的贝叶斯框架为合振荡器模型中的参数估计提供了一种有效的方法.
- 使用宏观数据 (库拉莫托顺序参数) 简化并可能改善参数估计.
- 该方法的性能强大,对噪声和系统大小有可预测的依赖.
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