A Tutorial on Concentration Bounds for System Identification
Optimization and Control
2019-08-30 v2 Machine Learning
Machine Learning
Abstract
We provide a brief tutorial on the use of concentration inequalities as they apply to system identification of state-space parameters of linear time invariant systems, with a focus on the fully observed setting. We draw upon tools from the theories of large-deviations and self-normalized martingales, and provide both data-dependent and independent bounds on the learning rate.
Cite
@article{arxiv.1906.11395,
title = {A Tutorial on Concentration Bounds for System Identification},
author = {Nikolai Matni and Stephen Tu},
journal= {arXiv preprint arXiv:1906.11395},
year = {2019}
}
Comments
Tutorial paper to appear at the 2019 IEEE Conference on Decision and Control