Abstract
Many time-resolved single-molecule biophysics experiments seek to characterize the kinetics of biomolecular systems exhibiting dynamics that challenge the time resolution of the given technique. Here, we present a general, computational approach to this problem that employs Bayesian inference to learn the underlying dynamics of such systems, even when they are much faster than the time resolution of the experimental technique being used. By accurately and precisely inferring rate constants, our Bayesian inference for the analysis of subtemporal resolution dynamics approach effectively enables the experimenter to super-resolve the poorly resolved dynamics that are present in their data.