Identifiability in continuous Lyapunov graphical models
Lyapunov graphical models represent a new approach in graphical modeling where independent observations are taken to be one-time cross-sectional snapshots of the multivariate Ornstein-Uhlenbeck process in equilibrium. The non-zero pattern of the drift matrix allows for a causally interpretable dependence structure among the coordinates of the process which can be represented by a directed graph.
After a short review of classical Gaussian linear structural equation models, we will introduce the Lyapunov models and focus on the fundamental question of identifiability, i.e. being able to recover the parameters knowing the true data generating distribution.
Based on joint work with Philipp Dettling, Mathias Drton, Niels Richard Hansen and Roser Homs