PAC-Bayesian-Like Error Bound for a Class of Linear Time-Invariant Stochastic State-Space Models
Machine Learning
2023-01-02 v1 Machine Learning
Dynamical Systems
Statistics Theory
Statistics Theory
Abstract
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.
Cite
@article{arxiv.2212.14838,
title = {PAC-Bayesian-Like Error Bound for a Class of Linear Time-Invariant Stochastic State-Space Models},
author = {Deividas Eringis and John Leth and Zheng-Hua Tan and Rafal Wisniewski and Mihaly Petreczky},
journal= {arXiv preprint arXiv:2212.14838},
year = {2023}
}