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Matteo Allione1, Vittorio Del Tatto1, Alessandro Laio1,2

  • 1Scuola Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, 34136 Trieste, Italy.

Physical review letters
|August 12, 2025
PubMed
概括

这项研究引入了一个新的框架,用于在复杂的动态系统中使用高维时间序列数据推断因果关系. 该方法通过将变量分组成"动态社区",有效地识别因果关系,减少计算挑战.

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