报告主题：复杂系统中的因果关系及统计建模
报告人：陈洛南 研究员 (中国科学院上海生命科学研究院)
报告时间：2017年4月28日（周五）10:00
报告地点：校本部F307
邀请人：许新建
主办部门：8455新葡萄场网站数学系
报告摘要：Quantifying causality between variables from observed time series data is of great importance in various disciplines. Unlike the conventional methods, we find it possible to detect causality only with very short time series data, based on embedding theory of an attractor for nonlinear dynamics. Specifically, we first show that measuring the smoothness of a cross map between two observed variables can be used to detect a causal relation. Then, we provide a very effective algorithm to computationally evaluate the smoothness of the cross map, and thus to infer the causality, which can achieve high accuracy even with very short time series data. Analysis of both mathematical models from various benchmarks and real data from biological systems validates our method.
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