Graphical continuous Lyapunov models

Code

• github.com/gherardovarando/gclm_experiments
  OfficialIn papernone★ 0
• github.com/gherardovarando/gclm
  OfficialIn papernone★ 0

Abstract

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 _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.

Reproductions