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Table 6 Estimation of divergence time T for model 3 in cases where the prior distribution does not encompass the true parameter value

From: Estimating demographic parameters from large-scale population genomic data using Approximate Bayesian Computation

True T With transformation Without transformation Without regression
  Mean 95% interval Mean 95% interval Mean 95% interval
0.60 0.4994 [0.4954, 0.5000] 0.6226 [0.0977, 1.1443] 0.4607 [0.3945, 0.4991]
0.65 0.5000 [0.5000, 0.5000] 0.5952 [0.5618, 0.6297] 0.4633 [0.4039, 0.4991]
0.70 0.5000 [0.5000, 0.5000] 0.7502 [0.5228, 0.9915] 0.4669 [0.4158, 0.4994]
0.75 0.5000 [0.5000, 0.5000] 0.5447 [0.4450, 0.6692] 0.4703 [0.4216, 0.4995]
0.80 0.5000 [0.5000, 0.5000] 1.2404 [0.4929, 1.8773] 0.4729 [0.4275, 0.4994]
0.85 0.5000 [0.5000, 0.5000] 0.7836 [0.5244, 0.9533] 0.4731 [0.4308, 0.4994]
0.90 0.5000 [0.5000, 0.5000] 0.6255 [0.5325, 0.7240] 0.4738 [0.4312, 0.4994]
0.95 0.5000 [0.5000, 0.5000] 0.6322 [0.4716, 0.7902] 0.4749 [0.4376, 0.4994]
1.00 0.5000 [0.5000, 0.5000] 0.6887 [0.5749, 0.8131] 0.4754 [0.4358, 0.4991]
  1. The means and the 95% credible intervals of the posterior samples are given for the ABC estimate using regression and transformation, using regression but without transformation, and without using regression (i.e., only using rejection). See text for a detailed explanation of the different versions of the ABC approach