在具有时空相关噪声的网络中进行估计
Sina Jahandari1, Jeffrey Shaman2
1Columbia University, New York, NY, USA.
概括
本研究引入了一种新的方法,用于估计与相关噪声的动态网络中的传递函数. 该方法使用图形理论,并引入隐藏的节点来识别最佳预测器输入,以准确估计.
科学领域:
- 网络分析 网络分析
- 系统识别 系统识别
- 图形理论是指图形的理论.
背景情况:
- 在动态网络中估计传输函数是具有挑战性的,因为相关的噪声.
- 现有的方法在网络节点之间与复杂的噪声结构作斗争.
研究的目的:
- 开发一种可靠的方法,用于在具有亲系相关噪声的动态网络中进行转移函数估计.
- 为了利用图形理论,特别是d分离,来选择最佳的预测器输入.
主要方法:
- 模拟空间噪声相关性作为隐藏的节点.
- 通过添加虚构节点系统地操纵网络结构.
- 应用d分离标准来确定一组一致的预测器输入.
主要成果:
- 提出了一个新的网络模型,保留了原来的时间序列属性.
- 为预测器输入选择制定了足够的图形条件.
- 选择的预测器通过预测错误方法来确保一致的转移函数估计.
结论:
- 拟议的方法有效地解决了在动态网络传输函数估计中的相关噪声.
- 使用图形操纵和d分离提供了一个理论上合理的方法.
- 这项工作在复杂网络的系统识别方面取得了重大进展.
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