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Table 1 Comparison of averaged error and mean count number for estimated rate constants over five iterations using algorithms 1 and 2 with simulation number of 10 for system 1.

From: Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density

Δt α\k   1 2 3 4 5
Algorithm 1
3 0.1 MN 15.41 7.21 7.36 8.21 10.05
   AE 0.7668 0.7294 0.7073 0.7832 0.6173
  0.05 MN 175.72 30.66 24.47 28.22 26.5
   AE 0.6120 0.5036 0.5521 0.7175 0.6132
  vary MN 46.46 25.07 22.76 30.09 88.56
   AE 0.7669 0.5306 0.6780 0.5858 0.5945
5 0.1 MN 26.96 10.47 9.07 11.18 13.19
   AE 0.7107 0.5607 0.5366 0.4693 0.4853
  0.05 MN 130.64 27.38 25.42 35.36 35.79
   AE 0.5826 0.6495 0.4260 0.7548 0.4139
  vary MN 141.97 30.28 53.47 127.16 2911.58
   AE 0.5587 0.4793 0.5416 0.5960 0.5375
Algorithm 2
3 0.05 MN 467.61 52.34 41.08 69.17 195.69
   AE 0.5834 0.6091 0.4867 0.4995 0.4402
  vary MN 100.26 32.04 24.78 80.15 1793.64
   AE 0.7132 0.6657 0.6305 0.6705 0.4833
5 0.05 MN 333.17 24.26 32.85 21.11 21.84
   AE 0.5962 0.5340 0.5761 0.4983 0.5518
  vary MN 243.78 22.6 31.29 34.6 70.25
   AE 0.6565 0.6035 0.5759 0.5488 0.4263
  1. Tests are experimented under different strategies of discrepancy tolerance such as α = 0.1, 0.05 or varies over iterations (AE:Averaged Error; MN: Mean count Number).
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BMC Bioinformatics

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