Bayesian ddLQR adds posterior uncertainty to the design, decomposing expected cost into certainty-equivalence plus variance terms, proving indirect-direct equivalence, and producing a data-length-independent SDP.
Regularization for covariance parameterization of direct data-driven lqr control,
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A Bayesian Perspective on the Data-Driven LQR
Bayesian ddLQR adds posterior uncertainty to the design, decomposing expected cost into certainty-equivalence plus variance terms, proving indirect-direct equivalence, and producing a data-length-independent SDP.