  In this paper we derive a PAC-Bayesian error bound for autonomous stochastic
LTI state-space models. The motivation for deriving such error bounds is that
they will allow deriving similar error bounds for more general dynamical
systems, including recurrent neural networks. In turn, PACBayesian error bounds
are known to be useful for analyzing machine learning algorithms and for
deriving new ones.
