用信息几何学分析因果关系:一个比较
Heng Jie Choong1, Eun-Jin Kim1, Fei He2
1Centre for Fluid and Complex Systems, Coventry University, Coventry CV1 5FB, UK.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
量化因果关系对于科学来说至关重要. 一种基于信息几何学的新信息速率因果关系方法,为非线性,非静止数据提供了一个优于格兰杰因果关系 (GC) 和转移 (TE) 的无模型方法.
科学领域:
- 复杂系统分析 复杂系统分析
- 信息理论是信息理论.
- 因果关系量化定量化.
背景情况:
- 对因果关系的量化对于理解复杂的现象至关重要,例如大脑网络和环境动态.
- 格兰杰因果关系 (GC) 和转移 (TE) 是常见的方法,但在非线性,非静止或非参数数据方面存在局限性.
- 现有的方法难以应对现实世界动态系统的复杂性.
研究的目的:
- 通过信息几何学来引入一种新的,无模型的方法来量化因果关系.
- 克服GC和TE等传统方法的局限性,特别是对于非线性和非静止时间序列数据.
- 开发一种强大的因果关系测量方法,适用于各种科学应用.
主要方法:
- 开发了一种基于信息几何学的信息速率因果关系 (IRC) 方法.
- 利用信息速率的概念,测量时间依赖分布的变化速率.
- 将IRC应用于数值生成的离散自回归模型,具有线性/非线性相互作用.
主要成果:
- 信息速率因果关系 (IRC) 通过检测过程分布的变化来有效地捕捉因果关系.
- 与GC和TE相比,IRC在线性和非线性时间序列数据中的合识别方面表现优越.
- 该方法适用于分析复杂,非静止和非线性数据集.
结论:
- 信息速率因果关系 (IRC) 为量化因果关系提供了一个强大的,无模型的替代方案.
- IRC克服了格兰杰因果关系 (GC) 和转移 (TE) 的关键局限性.
- 这种新方法增强了各种科学领域复杂动态系统的分析.
关键词:
格兰杰的因果关系转移 Entropy 是一个转移.有关因果关系的因果关系信息因果关系率因果关系率信息几何学信息几何学非静止的非静止的非线性模型是非线性模型.一个概率分布的概率分布.信号处理 信号处理 信号处理更多相关视频
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