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| Transfer Entropy× | Sampelentropi× | |
|---|---|---|
| Ämnesområde≠ | Kausal inferens | Komplexa system |
| Familj | Machine learning | Machine learning |
| Ursprungsår | 2000 | 2000 |
| Upphovsperson≠ | Thomas Schreiber | Richman & Moorman |
| Typ≠ | Non-parametric information-theoretic measure | Nonlinear entropy measure |
| Ursprungskälla≠ | Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85(2), 461–464. DOI ↗ | Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology, 278(6), H2039–H2049. DOI ↗ |
| Alias | Schreiber Information Transfer, Directed Information Flow, Conditional Mutual Information (directed), Transfer Entropisi | SampEn, Sample Entropy (SampEn), Örneklem Entropisi, Nonlinear Complexity Measure |
| Närliggande≠ | 3 | 2 |
| Sammanfattning≠ | Transfer Entropy (TE) is a non-parametric, information-theoretic measure of directed statistical dependence between two time series, introduced by Thomas Schreiber in 2000. Grounded in Shannon entropy, it quantifies how much information the past of one process Y reduces uncertainty about the next state of another process X, beyond what X's own past already provides. Unlike linear correlation or Granger causality, TE captures nonlinear interactions and requires no model assumptions about the underlying dynamics. | Sample Entropy (SampEn) is a nonlinear measure of the complexity and regularity of a time series. Introduced by Richman and Moorman in 2000 as an improvement over Approximate Entropy (ApEn), it quantifies the likelihood that similar patterns of a given length in the series remain similar when extended by one additional data point. A higher SampEn value indicates greater irregularity and complexity, while a lower value indicates more regularity or self-similarity. |
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