Inferring Parsimonious Coupling Statistics in Nonlinear Dynamics with Variational Gaussian Processes
利用变分高斯过程推断非线性动力系统的简约耦合统计量
Falsification is the basis for testing existing hypotheses, and a great danger is posed when results incorrectly reject our prior notions (false positives). Though nonparametric and nonlinear exploratory methods of uncovering coupling provide a flexible framework to study network configurations and discover causal graphs, multiple comparisons analyses make false positives more likely, exacerbating the need for their control. We aim to robustify the Gaussian Processes…