Fig. 2: Estimation of timescales with adaptive approximate Bayesian computations.

aABC estimates timescales by fitting the sample autocorrelation of observed data with a generative model. At the first iteration of the algorithm, parameters of the generative model are drawn from the multivariate prior distribution (for example, a uniform distribution; upper left). Synthetic data are generated from the generative model with these parameters. If the value of d between the autocorrelations of synthetic and observed data (computed up to maximum time-lag t_m) is smaller than a specific ε, these parameters are added to the multivariate posterior distribution (lower right). In subsequent iterations, new parameters are drawn from a proposal distribution, which is computed based on the posterior distribution of the previous iteration and the iniftial prior distribution (upper right, see Methods).