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Table 2 The average of performance of various model selection algorithms for the four simulation scenarios using four model selection methods in term of the fraction of correctly estimated link/non-links, i.e. false positives (FP), false negatives (FN), false discoveries (FD) and false non-discoveries (FnD), as well as the F1 = (2 - 2FN)/(2 - FN + FP) score, which measures the overall average accuracy of recall and precision. The best scores are indicated by bold font.

From: Inferring slowly-changing dynamic gene-regulatory networks

p   F P ¯ F N ¯ F D ¯ F n D ¯ F1score
20 AICc 0.0092 0.0811 0.2000 0.0031 0.9532
  BIC 0.0363 0.0139 0.4873 0.0005 0.9751
  AIC 0.0698 0.0069 0.6470 0.0003 0.9628
40 AICc 0.0057 0.0447 0.2899 0.0006 0.9743
  BIC 0.0088 0.0321 0.3826 0.0005 0.9793
  AIC 0.0437 0.0041 0.7514 0.0001 0.9766
60 AICc 0.0016 0.4585 0.2730 0.0036 0.7018
  BIC 0.0016 0.4585 0.2730 0.0036 0.7018
  AIC 0.0288 0.1452 0.8088 0.0012 0.9076
80 AICc 0.0091 0.1034 0.1680 0.0052 0.9410
  BIC 0.0396 0.0517 0.4527 0.0027 0.9541
  AIC 0.0670 0.0000 0.5704 0.0000 0.9675
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