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Table 2 Comparison of averaged error and mean count number for estimated rate constants of system 2 using algorithms 1 and 2.

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
2 0.05 MN 18.29 7.53 9.8 12.7 14.23
   AE 4.6211 4.4179 4.7138 4.2188 3.8119
  Same k MN 2.69 2.07 2.16 1.93 1.93
   AE 4.7006 4.9603 4.8841 4.6833 4.7298
  Diff. k MN 15.26 7.85 8.78 13.06 12.28
   AE 4.8295 4.5322 5.0418 4.7346 4.6069
5 0.05 MN 9.69 3.48 3.12 58.2 74.07
   AE 4.1076 4.3243 4.1868 3.5311 3.5194
  Same k MN 2.34 2.31 2.42 16.9 11.38
   AE 4.9862 4.7669 4.6716 3.8873 4.0017
  Diff. k MN 25.72 8.14 10.45 25.8 174.88
   AE 4.0461 3.9583 3.7474 3.5655 3.6951
Algorithm 2
2 0.05 MN 89.7 19.75 17.8 40.42 69.52
   AE 4.0540 4.1339 4.1376 3.9696 3.9009
  Same k MN 2.52 3.85 3.55 3.82 3.84
   AE 5.0456 4.6069 4.3666 4.5876 3.8958
  Diff. k MN 197.49 15.05 22.09 36.85 94.24
   AE 3.8712 3.7934 4.3158 3.6485 3.5989
5 0.05 MN 138.14 30.52 46.66 98.87 377.66
   AE 4.0258 3.7218 3.8258 3.8445 3.9205
  Same k MN 21.67 11.34 11.17 26.65 59.64
   AE 4.0545 3.5715 4.1910 3.7252 3.8667
  Diff. k MN 185.54 28.39 33.81 89.81 846.61
   AE 3.7810 3.6694 3.6939 3.9806 3.8515
  1. Three strategies are used to choose the discrepancy tolerance α: a fixed value of α= 0.05; varying α values; and α= k denoted as same k ); varying α values that are smaller than k (denoted as diff. k ).(AE:Averaged Error; MN: Mean count Number).