Both methods are based on the data from a single run. As a result, both are susceptible to erroneously discarding the majority of simulation data when the system becomes trapped in a local minimum. Convergence is typically assessed using uncertainties obtained from a single run (after block-averaging or sub-sampling to remove correlation) or, more rigorously, from differences between repeated runs. Despite similarity of the underlying problem, different diagnostics are used in the Markov Chain Monte Carlo (MCMC) literature. The most common method for assessing sampling seems to be the Gelman-Rubin diagnostic, which effectively compares the variances estimated within and between MCMC chains. If individual chains (simulations) are initially overdispersed and become trapped in local minima, the inter-chain variances will be greater than the intra-chain variances. This manifests as a Gelman-Rubin R > 1, indicating that individual chains have failed to converge to the same stationary distribution. MCMC equilibration detection methods include the Geweke test, which checks for a significant difference between the means of the initial and final portions of the data. These diagnostics have occasionally been used in molecular simulation (for example by García and Hasse) but, to our knowledge, they are yet to be exploited in free energy calculations.