Mechanistic priors reduce Bayesian regret in sequential decisions by scaling with residual entropy H_mech, yielding a sample complexity reduction of H(μ)/H_mech asymptotically and lower bounds on penalties in the burn-in regime, with gains shown in 5-FU dosing simulations.
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The Value of Mechanistic Priors in Sequential Decision Making
Mechanistic priors reduce Bayesian regret in sequential decisions by scaling with residual entropy H_mech, yielding a sample complexity reduction of H(μ)/H_mech asymptotically and lower bounds on penalties in the burn-in regime, with gains shown in 5-FU dosing simulations.