不确定性 数据充足性的网络推理量化
Bharat Singhal1, Jorge Luis Ocampo-Espindola2, K L Nikhil3
1Department of Electrical and Systems Engineering, Washington University in St Louis, St. Louis, Missouri 63130, USA.
IEEE transactions on network science and engineering
|August 28, 2025
概括
确定数据充足度对于准确的网络推断至关重要. 本研究引入了一种使用置信区间量化数据可变性的统计方法,确保可靠的网络拓重建.
科学领域:
- 复杂系统科学
- 网络科学
- 统计推断
背景情况:
- 网络推断从数据中重建系统连接,对于理解物理,生物和化学系统至关重要.
- 目前的数据驱动方法往往忽视了准确网络拓的数据充足性的关键问题.
- 准确的网络重建需要足够的数据可变性来可靠地推断底层结构.
研究的目的:
- 开发一个统计方法来评估网络推断中的数据充分性.
- 根据数据变化量化推断网络连接的不确定性.
- 确保推断的网络拓准确地反映了真正的基础网络结构.
主要方法:
- 使用参数置信区间来定义真实连接强度的边界.
- 开发一种技术来评估网络推断准确性的数据可变性.
- 借助推断连接的不确定性量化.
主要成果:
- 拟议的统计方法有效地确定了网络推断的数据充分性.
- 在Kuramoto和Stuart-Landau振荡器网络上的验证证明了方法的准确性.
- 对实验电化学振荡器网络数据的成功应用证实了预测能力.
结论:
- 开发的数据充足技术对于可靠的网络推断至关重要.
- 这种方法提高了网络拓重建的可靠性.
- 它提供了一种定量衡量,以确保足够的数据用于准确的系统分析.
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