Table 4. Approximate Bayesian Computation mean posterior parameter errors under the winning Scenario AfrDE-EurDE, for the ACB and ASW populations separately, using four different methods: NN estimation of the parameters taken jointly as a vector, NN estimation of the parameters taken separately, Random Forest (parameters taken separately), and Rejection (parameters taken separately). For each target population separately and for each method, we conducted an out-of-bag cross validation by considering in turn 1,000 separate parameter inferences each using one of the 1,000 closest simulation to the observed ACB (or ASW) data as the target pseudo-observed dataset. All posterior parameter estimations were conducted using the other 99,999 simulations under the AfrDE-EurDE scenario (Figure 1 ,Table 1 ), a 1% tolerance rate (i.e. 1,000 simulations), 24 summary statistics, logit transformation of all parameters, four neurons in the hidden layer per neural network and 500 trees per random forest. 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).