传递熵
连接词(语言学)
熵(时间箭头)
条件熵
参数统计
条件独立性
广义熵指数
联合熵
计算机科学
信息图表
统计物理学
数学
计量经济学
数据挖掘
统计
最大熵热力学
最大熵原理
二元熵函数
物理
热力学
面板数据
出处
期刊:Cornell University - arXiv
日期:2019-10-10
被引量:2
摘要
Causal discovery is a fundamental problem in statistics and has wide applications in different fields. Transfer Entropy (TE) is a important notion defined for measuring causality, which is essentially conditional Mutual Information (MI). Copula Entropy (CE) is a theory on measurement of statistical independence and is equivalent to MI. In this paper, we prove that TE can be represented with only CE and then propose a non-parametric method for estimating TE via CE. The proposed method was applied to analyze the Beijing PM2.5 data in the experiments. Experimental results show that the proposed method can infer causality relationships from data effectively and hence help to understand the data better.
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