Table 3. Neural-Network Approximate Bayesian Computation posterior parameter errors under the winning scenario AfrDE-EurDE, for the ACB and ASW populations. For each target population separately, we conducted cross-validation by considering in turn 1,000 separate NN-ABC parameter inferences each using in turn one of the 1,000 closest simulations to the observed ACB (or ASW) data as the target pseudo-observed simulation. All posterior parameter estimations were conducted using 100,000 simulations under scenario AfrDE-EurDE (Figure 1 , Table 1 ), a 1% tolerance rate (1,000 simulations), 24 summary statistics, logit transformation of all parameters, and four neurons in the hidden layer (see Materials and Methods ). Median was considered as the point posterior parameter estimation for all parameters. First column provides the average absolute error; second column shows the mean-squared error; third column shows the mean-squared error scaled by the parameter’s observed variance (see Materials and Methods for error formulas).