Causal Geometry
暂无分享,去创建一个
[1] Alison S. Tomlin,et al. A systematic lumping approach for the reduction of comprehensive kinetic models , 2005 .
[2] F. Wilczek. Quantum Field Theory , 1998, hep-th/9803075.
[3] Olaf Sporns,et al. Measuring information integration , 2003, BMC Neuroscience.
[4] Jens Timmer,et al. Driving the Model to Its Limit: Profile Likelihood Based Model Reduction , 2016, PloS one.
[5] P. Anderson. More is different. , 1972, Science.
[6] Albert-László Barabási,et al. Controllability of complex networks , 2011, Nature.
[7] L. Ryder,et al. Quantum Field Theory , 2001, Foundations of Modern Physics.
[8] L. Goldberg. The Book of Why: The New Science of Cause and Effect† , 2019, Quantitative Finance.
[9] Mark K Transtrum,et al. Model reduction by manifold boundaries. , 2014, Physical review letters.
[10] J. Sethna,et al. Information loss under coarse graining: A geometric approach , 2017, Physical Review E.
[11] Mark K Transtrum,et al. Geometry of nonlinear least squares with applications to sloppy models and optimization. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.
[12] Joseph Y. Halpern,et al. Actual Causality , 2016, A Logical Theory of Causality.
[13] R. D. Portugal,et al. Weber-Fechner Law and the Optimality of the Logarithmic Scale , 2011, Minds and Machines.
[14] Hector Zenil,et al. Empirical Encounters with Computational Irreducibility and Unpredictability , 2011, Minds and Machines.
[15] J. Pearl,et al. The Book of Why: The New Science of Cause and Effect , 2018 .
[16] David Pines,et al. From the Cover : The Theory of Everything , 1999 .
[17] George Sugihara,et al. Detecting Causality in Complex Ecosystems , 2012, Science.
[18] Erik P. Hoel. When the Map Is Better Than the Territory , 2016, Entropy.
[19] Wenye Hu,et al. Dimming curve based on the detectability and acceptability of illuminance differences. , 2016, Optics express.
[20] Michael C. Abbott,et al. Maximizing the information learned from finite data selects a simple model , 2017, Proceedings of the National Academy of Sciences.
[21] David J. C. MacKay,et al. Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.
[22] A. Antoulas,et al. A Survey of Model Reduction by Balanced Truncation and Some New Results , 2004 .
[23] Navot Israeli,et al. Computational irreducibility and the predictability of complex physical systems. , 2003, Physical review letters.
[24] D. Pines,et al. The theory of everything. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[25] Erik P. Hoel. Agent Above, Atom Below: How Agents Causally Emerge from Their Underlying Microphysics , 2018 .
[26] David Balduzzi,et al. Information, learning and falsification , 2011, NIPS 2011.
[27] Sam Subbey,et al. An improved methodology for quantifying causality in complex ecological systems , 2019, PloS one.
[28] Temple F. Smith. Occam's razor , 1980, Nature.
[29] Bryan C. Daniels,et al. Perspective: Sloppiness and emergent theories in physics, biology, and beyond. , 2015, The Journal of chemical physics.
[30] Shun-ichi Amari,et al. Information Geometry and Its Applications , 2016 .
[31] Sabine U. König,et al. Embodied cognition , 2018, 2018 6th International Conference on Brain-Computer Interface (BCI).
[32] J. Sethna,et al. Parameter Space Compression Underlies Emergent Theories and Predictive Models , 2013, Science.
[33] G. Box. Science and Statistics , 1976 .
[34] Larissa Albantakis,et al. Causal Composition: Structural Differences among Dynamically Equivalent Systems , 2019, Entropy.
[35] Bryan C. Daniels,et al. Automated adaptive inference of phenomenological dynamical models , 2015, Nature Communications.
[36] Heather A. Harrington,et al. The geometry of Sloppiness , 2016, Journal of Algebraic Statistics.
[37] Erik P. Hoel,et al. Quantifying causal emergence shows that macro can beat micro , 2013, Proceedings of the National Academy of Sciences.
[38] Christopher R. Myers,et al. Universally Sloppy Parameter Sensitivities in Systems Biology Models , 2007, PLoS Comput. Biol..
[39] Ricard V. Solé,et al. Phase transitions and complex systems: Simple, nonlinear models capture complex systems at the edge of chaos , 1996, Complex..