Efficient and passive learning of networked dynamical systems driven by non-white exogenous inputs. (arXiv:2110.00852v3 [cs.LG] UPDATED)

We consider a networked linear dynamical system with $p$ agents/nodes. We
study the problem of learning the underlying graph of interactions/dependencies
from observations of the nodal trajectories over a time-interval $T$. We
present a regularized non-casual consistent estimator for this problem and
analyze its sample complexity over two regimes: (a) where the interval $T$
consists of $n$ i.i.d. observation windows of length $T/n$ (restart and
record), and (b) where $T$ is one continuous observation window (consecutive).
Using the theory of …

arxiv exogenous learning systems