{"id":32304,"date":"2022-02-07T22:36:26","date_gmt":"2022-02-07T14:36:26","guid":{"rendered":"https:\/\/swarma.org\/?p=32304"},"modified":"2022-02-07T22:36:26","modified_gmt":"2022-02-07T14:36:26","slug":"%e5%9c%b0%e7%90%83%e6%b0%94%e5%80%99%e7%b3%bb%e7%bb%9f%e4%b8%ad%e7%9a%84%e5%9b%a0%e6%9e%9c%e6%8e%a8%e6%96%ad-%e5%91%a8%e4%ba%8c%e7%9b%b4%e6%92%ad%c2%b7%e5%9c%b0%e7%90%83%e7%b3%bb%e7%bb%9f%e7%a7%91","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=32304","title":{"rendered":"\u5730\u7403\u6c14\u5019\u7cfb\u7edf\u4e2d\u7684\u56e0\u679c\u63a8\u65ad | \u5468\u4e8c\u76f4\u64ad\u00b7\u5730\u7403\u7cfb\u7edf\u79d1\u5b66\u8bfb\u4e66\u4f1a"},"content":{"rendered":"
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\u53c2\u8003\u6587\u732e\uff08\u5305\u62ec\u4e66\u7c4d\u3001\u8bba\u6587 MLA\u683c\u5f0f\uff09<\/span><\/strong><\/span><\/h1>\n
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  1. \n

    Runge, J., Bathiany, S., Bollt, E. et al. Inferring causation from time series in Earth system sciences. Nat Commun 10, 2553 (2019).<\/span><\/p>\n<\/li>\n

  2. \n

    van Nes, E., Scheffer, M., Brovkin, V. et al. Causal feedbacks in climate change. Nature Clim Change 5, 445\u2013448 (2015).<\/span><\/p>\n<\/li>\n

  3. \n

    Tsonis AA, Deyle ER, May RM, Sugihara G, Swanson K, Verbeten JD, Wang G. Dynamical evidence for causality between galactic cosmic rays and interannual variation in global temperature. Proc Natl Acad Sci U S A. 2015 Mar 17;112(11):3253-6.<\/span><\/p>\n<\/li>\n

  4. \n

    Detecting Causality in Complex Ecosystems. Science, 338.6016, 496-500 (2012).<\/span><\/p>\n<\/li>\n

  5. \n

    Ebert-Uphoff, I., & Deng, Y. (2012). Causal Discovery for Climate Research Using Graphical Models, Journal of Climate, 25(17), 5648-5665.<\/span><\/p>\n<\/li>\n

  6. \n

    McGraw, M. C., & Barnes, E. A. (2018). Memory Matters: A Case for Granger Causality in Climate Variability Studies, Journal of Climate, 31(8), 3289-3300.<\/span><\/p>\n<\/li>\n

  7. \n

    Ye, H., Deyle, E., Gilarranz, L. et al. Distinguishing time-delayed causal interactions using convergent cross mapping. Sci Rep 5, 14750 (2015).<\/span><\/p>\n<\/li>\n

  8. \n

    Jakob Runge, Peer Nowack, Marlene Kretschmer, Seth Flaxman, Dino Sejdinovic, Detecting and quantifying causal associations in large nonlinear time series datasets, Science Advances 5, eaau4996 (2019).<\/span><\/p>\n<\/li>\n

  9. \n

    Zhixin Lu, Jaideep Pathak, Brian Hunt, Michelle Girvan, Roger Brockett, and Edward Ott , “Reservoir observers: Model-free inference of unmeasured variables in chaotic systems”, Chaos 27, 041102 (2017)<\/span><\/p>\n<\/li>\n

  10. \n

    Yu Huang, Zuntao Fu, and Christian L. E. Franzke , “Detecting causality from time series in a machine learning framework”, Chaos 30, 063116 (2020).<\/span><\/p>\n<\/li>\n

  11. \n

    Mingzhao Wang, Zuntao Fu,A new method of nonlinear causality detection: Reservoir computing Granger causality, Chaos, Solitons & Fractals, Volume 154 (2022).<\/span><\/p>\n<\/li>\n<\/ol>\n


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