Figure : The creation of synthetic datasets modelled on real data. a) A 30 ns time series of free energy change estimaters against simulation time was obtained from an absolute binding free energy calculation (here we averaged over 5 repeats for the Lennard-Jones term removal stage when the ligand is bound to the protein). The initial transient was captured with a biexponential fit. The final 20 ns generally showed no substantial overall trend and were approximately Gaussian. These data were used to fit an autocovariance function. b) To generate synthetic time series, we started with a vector of uncorrelated Gaussian noise. The desired autocorrelation structure was reintroduced by taking its product with the Cholesky decomposition of the autocovariance matrix, and the initial transient was reintroduced by adding the biexponential fit. This was repeated N times with different Gaussian noise (but the same autocovariance function and biexponential trend) to generate N synthetic time series. We used N = 1000.