| 第1,158行： |
第1,158行： |
| | | | |
| | == 参考文献列表 == | | == 参考文献列表 == |
| − | 1. 信息论概念的参考资料：Papoulis, A., & Pillai, S. U. (2002). Probability, Random Variables, and Stochastic Processes (4th ed.). McGraw-Hill
| |
| − |
| |
| − | 2.总相关性概念的原文:Information theoretical analysis of multivariate correlation Satosi Watanabe IBM Journal of Research and Development, Volume 4, Issue 1 Pages 66 - 82 <nowiki>https://doi.org/10.1147/rd.41.0066</nowiki>
| |
| − |
| |
| − | 3.McGill 交互信息概念的原文:McGill, W. J. (1954). Multivariate information transmission. Psychometrika'', 19,'' 97–116. <nowiki>https://doi.org/10.1007/BF02289159</nowiki>
| |
| − |
| |
| − | 4.McGill 交互信息图片引用:https://en.wikipedia.org/wiki/Interaction_information
| |
| − |
| |
| − | 5.PID原文:Paul L Williams and Randall D Beer. Nonnegative decomposition of multivariate information. arXiv preprint arXiv:1004.2515, 2010.'', 2010.''
| |
| − |
| |
| − | 6.Lyu AoBo等研究者对PID框架冗余晶格结构的批评:R. Wibral, J. T. Lizier, and P. L. Williams, "Multivariate partial information decomposition: Constructions, inconsistencies, and alternative measures," Phys. Rev. E 105, 014101 (2022). <nowiki>https://doi.org/10.1103/PhysRevE.105.014101</nowiki>
| |
| − |
| |
| − | 7.~G-PVenkatesh, P., Bennett, C., Gale, S., Ramirez, T., Heller, G., Durand, S., Olsen, S., & Mihalas, S. (2023). Gaussian partial information decomposition: Bias correction and application to high-dimensional data. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), ''Advances in Neural Information Processing Systems'' (Vol. 36, pp. 50409–50422). Curran Associates.ciates,
| |
| − |
| |
| − | 8.Thin-PID&Flow-Zhao, W., Balachandran, A., Tian, C., & Liang, P. P. (2025). Partial information decomposition via normalizing flows in latent Gaussian distributions. In Advances in Neural Information Processing Systems (Vol. 38). Curran Associates. <nowiki>https://doi.org/10.48550/arXiv.2510.04417ciates</nowiki>,
| |
| − |
| |
| − | 9.给复杂系统科学家的信息论 综述:Varley T F. Information theory for complex systems scientists[J]. arXiv preprint arXiv:2304.12482, 2023.
| |
| − |
| |
| − | 10.SURD方法原文：Martínez-Sánchez, Á., Arranz, G., & Lozano-Durán, A. (2024). Decomposing causality into its synergistic, unique, and redundant components. arXiv:2405.12411.
| |
| − |
| |
| − | 11.杨明哲介绍SURD:因果上的信息分解 | 集智斑图[https://pattern.swarma.org/study_group_issue/894 https://pattern.swarma.org/study_group_is]
| |
| − |
| |
| − | [https://pattern.swarma.org/study_group_issue/894 e/894]
| |
| − |
| |
| − | 12.信息分解在因果涌现中的应用综述：Mediano, P. A. M., Rosas, F. E., Luppi, A. I., Jensen, H. J., Seth, A. K., Barrett, A. B., Carhart-Harris, R. L., & Bor, D. (2022). Greater than the parts: A review of the information decomposition approach to causal emergence. Philosophical Transactions of the Royal Society A, 380(20210246). <nowiki>https://doi.org/10.1098/rsta.2021.0246</nowiki>
| |
| − |
| |
| − | 13.基因网络中的因果涌现:Pigozzi, F., Goldstein, A., & Levin, M. (2025). Associative conditioning in gene regulatory network models increases integrative causal emerCommunications Biology, 8'', ology'', ''8''(1), 1027.
| |
| − |
| |
| − | 14.王志鹏介绍整合信息分解框架在脑科学/神经科学中的应用:大脑演化和认知的协同核<nowiki/>https://pattern.swarma.org/study_group_issue/692 | 集智斑图
| |
| − |
| |
| − | 15.Phi ID 用于脑科学研究：Pigozzi, F., Goldstein, A., & Levin, M. (2025). Associative conditioning in gene regulatory network models increases integrative causal emergence. Communications Biology, 8(1), 1027. <nowiki>https://doi.org/10.1038/s42003-025-01027-5</nowiki>
| |
| − |
| |
| − | 16.Phi ID 用于意识研究：Andrea I Luppi, Pedro A M Mediano, Fernando E Rosas, David J Harrison, Robin L Carhart-Harris, Daniel Bor, Emmanuel A Stamatakis, What it is like to be a bit: an integrated information decomposition account of emergent mental phenomena, Neuroscience of Consciousness, Volume 2''''', Volume 2'''''021, Issue 2, 2021, niab027, <nowiki>https://doi.org/10.1093/nc15./nia框架bRD：Phys</nowiki>. Rev. Lett. 135, 187401 – Published 29 October, 2025 DOI: <nowiki>https://doi.org/101103</nowiki>
| |
| − |
| |
| − | 17.L. Faes, L. Sparacino, G. Mijatvic, Y. Antonacci, L. Ricci, D. Marinazzo, and S. Stramaglia, "Partial Information Rate Demposition," Phys. Rev. Lett. 135, 187401 (2025). <nowiki>https://link.aps.org/doi/10.1103/nrwj-n8lj</nowiki>r6j-n8lj
| |
| − |
| |
| − | 18.PIXiu, L., Luo, X., & Nakayma, H. (2026). A comprehensive information-decomposition analysis of large vision-lnguage models. In *Proceedings of the International Conference on Learning Representations (ICLR 2026)*. OpenReview. <nowiki>https://openreview.net/forum?id=7lI3XhYQ5i</nowiki>
| |
| − |
| |
| − | 信息原子的数值计算程序：1.https://towardsdatascience.com/what-is-partial-information-decomposition-and-how-features-interact-a713456a1029/
| |
| − |
| |
| − | 2.专门实现PID计算的pietromarchesi库：https://github.com/pietromarchesi/pidpy
| |
| | <references /> | | <references /> |