For this reason, there has been much interest in using Bayesian inference and/or maximumentropy approaches to infer forward model uncertainties directly from data. Bayesian Inference of Conformational Populations (BICePs) algorithm is an alternative approach to force field evaluation that uses Markov chain Monte Carlo (MCMC) sampling to infer the posterior distribution of each σ j . Using the most recent version of BICePs, Raddi et al. implemented a replica-averaging approach to achieve maximum-entropy reweighting of the chignolin conformational populations derived from our work here. This approach enables the calculation of quantity called the BICePs score that provides an objective measure of model quality. Conceptually, the BICePs score can be conceived as the free energy cost of "turning on" a given set of conformational populations alongside a set of experimental restraints. BICePs scores calculated for the same nine force fields by Raddi et al. show similar trends with the computed reduced chi-squared values computed here, but without the requirement of prior knowledge about the uncertainties for experimental observables, nor for the forward models.