Bayesian System ID: Optimal management of parameter, model, and measurement uncertainty

4 Mar 2020Nicholas GaliotoAlex Gorodetsky

We evaluate the robustness of a probabilistic formulation of system identification (ID) to sparse, noisy, and indirect data. Specifically, we compare estimators of future system behavior derived from the Bayesian posterior of a learning problem to several commonly used least squares-based optimization objectives used in system ID... (read more)

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