Machine learningInformation-theoretic causality
转移熵
转移熵(TE)是一种非参数、信息论的度量,用于衡量两个时间序列之间的定向统计依赖性,由Thomas Schreiber于2000年提出。它以香农熵为基础,量化了过程Y的过去在多大程度上减少了过程X的下一个状态的不确定性,超出了X自身的过去已经提供的信息。与线性相关或格兰杰因果关系不同,TE能够捕捉非线性相互作用,并且不需要对潜在动力学进行模型假设。
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来源
- Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85(2), 461–464. DOI: 10.1103/PhysRevLett.85.461 ↗
如何引用本页
ScholarGate. (2026, June 2). Transfer Entropy. ScholarGate. https://scholargate.app/zh/causal-inference/transfer-entropy
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