Salīdzināt metodes

	Pārneses entropija	Entropija pēc parauga
Nozare	Cēloņsakarību secināšana	Kompleksās sistēmas
Saime	Machine learning	Machine learning
Izcelsmes gads	2000	2000
Autors	Thomas Schreiber	Richman & Moorman
Tips	Non-parametric information-theoretic measure	Nonlinear entropy measure
Pirmavots	Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85(2), 461–464.	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.
Citi nosaukumi	Schreiber Information Transfer, Directed Information Flow, Conditional Mutual Information (directed), Transfer Entropisi	SampEn, Sample Entropy (SampEn), Örneklem Entropisi, Nonlinear Complexity Measure
Saistītās	3	2
Kopsavilkums	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.