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PAC-Bayesian Bounds for Learning Partially Observed Stochastic Linear Time-Invariant State-Space Systems with Inputs and Sub-Gaussian Noise
PAC-Bayesian bounds relate expected and empirical prediction errors for partially observed LTI state-space systems with sub-Gaussian noise, yielding finite-sample guarantees for system identification and parameter estimation.
Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026
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