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Table 2 Parameter inference result for auto-regulatory gene network model (Fully observed case)

From: Parameter inference for discretely observed stochastic kinetic models using stochastic gradient descent

  k 1 k 2 k 3 k 4 k 5 k 6 k 7 k 8 Average % Err.
Datasets *(0.1 0.7 0.35 0.3 0.1 0.9 0.2 0.1)  
D1, Δt = 1.0 0.114 0.81 0.346 0.229 0.051 0.418 0.221 0.074 24.2
D2, Δt = 1.0 0.094 0.72 0.435 0.344 0.052 0.485 0.265 0.119 24.2
D3, Δt = 0.5 0.113 0.82 0.408 0.321 0.075 0.75 0.226 0.095 14.7
D4, Δt = 0.5 0.113 0.71 0.276 0.253 0.086 0.77 0.223 0.100 11.4
D5, Δt = 0.1 0.079 0.74 0.349 0.286 0.101 0.86 0.183 0.094 6.4
D6, Δt = 1.0 0.095 0.42 0.321 0.277 0.10 0.73 0.235 0.104 12.7
D7, Δt = 1.0 0.097 0.90 0.35 0.335 0.079 0.92 0.312 0.12 17.8
D8, Δt = 0.5 0.120 0.40 0.52 0.38 0.092 0.998 0.215 0.081 22.9
D9, Δt = 0.5 0.116 0.96 0.41 0.41 0.101 1.01 0.144 0.094 19.3
D10, Δt = 0.1 0.052 0.91 0.277 0.35 0.128 0.93 0.137 0.075 25.4
  1. * True values of parameters
  2. Total observation time window = 50. For datasets D1-D5: DNAt = 10; D6-D10:DNAt = 2.
  3. Average % Err. ≡ |k i - k i, true |/k i, true i