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Table 3 MCMC model comparison results for the artificial FF datasets.

From: Statistical model comparison applied to common network motifs

data source measure CONTROL FF.C1.AND
(Eqns 15)
FF.C1.OR.1
(Eqns 18)
FF.I1.AND
(Eqns 24)
   (Eqn. 17)    
FF.C1.AND
(Eqns 15)
log p(Y | M i ) 85.98 108.94 106.51 86.53
  DIC -217.98 -388.91 -292.81 -205.64
  log p(Y | θ ML , M i ) 111.22 165.18 164.34 111.15
  AIC -208.44 -316.36 -314.68 -208.3
FF.C1.OR.1
(Eqns 18)
log p(Y | M i ) (Eqn. 20)
40.12
30.90 48.09 42.55
  DIC -108.96 -102.47 -136.65 -117.22
  log p(Y | θ ML , M i ) 62.73 60.06 86.94 69.97
  AIC -111.46 -106.12 -159.88 -125.94
FF.I1.AND
(Eqns 24)
log p(Y | M i ) (Eqn. 26)
12.52
8.28 13.78 14.02
  DIC -38.33 -35.75 -36.75 -41.63
  log p(Y | θ ML , M i ) 26.66 25.69 30.09 30.86
  AIC -39.32 -37.38 -46.18 -47.72
  1. Datasets have the same number of samples as the experimental data from [57]. They were generated using Equations 15 (first row), 18 (second row) and 24 (third row). Note that the model labelled control is specific for each dataset: ara control (Equation 17) on the first row, flagella control (Equation 20) on the second row, and gal control (Equation 26) on the last row.