When the simulation's predicted ⟨A⟩ disagrees with the experimental A * , we aim to identify an ensemble Z ME 0 that introduces the least perturbation to the original ensemble while aligning with the experimental data. We measure the perturbation to the original ensemble using the Kullback-Leibler (KL) divergence D KL that quantifies the distance between the two ensembles. It is given by To fit experimental data, these weights must satisfy the normalization condition