A Tutorial on Concentration Bounds for System Identification¶
Authors: Nikolai Matni, Stephen Tu
Published: 2019 ()
arXiv: 1906.11395
Summary¶
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.