  In this paper we derive a PAC-Bayesian-Like error bound for a class of
stochastic dynamical systems with inputs, namely, for linear time-invariant
stochastic state-space models (stochastic LTI systems for short). This class of
systems is widely used in control engineering and econometrics, in particular,
they represent a special case of recurrent neural networks. In this paper we 1)
formalize the learning problem for stochastic LTI systems with inputs, 2)
derive a PAC-Bayesian-Like error bound for such systems, 3) discuss various
consequences of this error bound.
