where D is the current dataset of observations. The fictitious measurement y ′ is then used to augment the dataset of observations, i.e. D ′ = D ∪ {(x ′ , y ′ )}. Effectively, this strategy fixes the posterior mean of the surrogate model and updates its variance in light of pending parameters. Atlas maintains a "priority queue" of recommended parameters that is immediately updated in light of new measurements, such that parameter proposals are always informed by the most recent observations. Proposals in the priority queue are then delegated to measurement workers as they become available.