A Tutorial on Concentration Bounds for System Identification
arXiv:1906.11395v248 citations
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This is an incremental tutorial for researchers in system identification, offering a consolidated guide on using advanced statistical tools.
The paper provides a tutorial on applying concentration inequalities to estimate parameters of linear time-invariant systems, focusing on deriving both data-dependent and independent bounds for learning rates.
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.