“…Monte Carlo methods are amenable to approximating solutions to problems that allow a probabilistic interpretation. They have vast applications in uncertainty quantification [42, 75-77, 79, 95], Bayesian statistics [20,32,42,55,93,95] (and machine learning [4,95], more broadly), and computational physics [2,3,10,20,31,44,45,50,53,54,56,62,63,73,80,82,83,87,90,91]. While the focus of this work will be on equilibrium statistical mechanics, the proposed method for accelerated importance sampling is not specific to physical applications.…”