Sample Complexity of the Sign-Perturbed Sums Identification Method: Scalar Case

no code implementations • 28 Jan 2024 • Szabolcs Szentpéteri, Balázs Csanád Csáji

Sign-Perturbed Sum (SPS) is a powerful finite-sample system identification algorithm which can construct confidence regions for the true data generating system with exact coverage probabilities, for any finite sample size.

Non-Asymptotic State-Space Identification of Closed-Loop Stochastic Linear Systems using Instrumental Variables

no code implementations • 29 Jan 2023 • Szabolcs Szentpéteri, Balázs Csanád Csáji

The paper suggests a generalization of the Sign-Perturbed Sums (SPS) finite sample system identification method for the identification of closed-loop observable stochastic linear systems in state-space form.