RMSE: ABC algorithm validation for estimator bias and precision. RMSE (root mean square error) across 100 estimates of parameter values given 100 PODS (pseudo observed data sets) simulated with known parameter values. Panel A corresponds to estimates of E(τ) and panel B corresponds to estimates of Ω. The error bars depict 2 × SD (standard deviation) of the RMSE across each set of 100 estimates. For all PODS, Ψ (number divergence times across five taxon-pairs) is drawn from its discrete uniform hyper-prior ranging between 1 (simultaneous divergence) and 5 (the number of taxon-pairs). PODS and corresponding priors were simulated given data from 1, 4, 8, 16, 32 and 64 loci each from 5 taxon-pairs. Each RMSE is calculated from the 100 true hyper-parameter values (E(τ) and Ω) and the corresponding 100 posterior mode estimates (mode from the 500 accepted points out of a total 1,500,000 draws from the hyper-prior using ABC with local linear regression and a summary statistic vector D
that only included mean values of π
across loci from every taxon-pair).