论文标题
图形连续Lyapunov模型
Graphical continuous Lyapunov models
论文作者
论文摘要
协方差矩阵的线性lyapunov方程参数参数随机过程的平衡协方差矩阵。该参数化可以解释为新的图形模型类，我们展示了模型类在边缘化下的行为，并引入了一种通过$ \ ell_1 $ penalizatization sloughtization thoughtization trimighation的结构学习方法。我们提出的方法证明了在仿真研究中胜过替代结构学习算法的表现，我们说明了其在蛋白质磷酸化网络重建中的应用。
The linear Lyapunov equation of a covariance matrix parametrizes the equilibrium covariance matrix of a stochastic process. This parametrization can be interpreted as a new graphical model class, and we show how the model class behaves under marginalization and introduce a method for structure learning via $\ell_1$-penalized loss minimization. Our proposed method is demonstrated to outperform alternative structure learning algorithms in a simulation study, and we illustrate its application for protein phosphorylation network reconstruction.
