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Table 3 The models used to simulate pseudo-replicate datasets for assessing the power of the models in Table 2

From: An improved approximate-Bayesian model-choice method for estimating shared evolutionary history

  Priors
Model series t τ θ  
msBayes |τ|=22 τU(0,0.2 [ 0.5 M G A]) θ A U(0,0.05) θ ̄ D U(0,0.05)
  |τ|=22 τU(0,0.4 [ 1.0 M G A]) θ A U(0,0.05) θ ̄ D U(0,0.05)
  |τ|=22 τU(0,0.6 [ 1.5 M G A]) θ A U(0,0.05) θ ̄ D U(0,0.05)
  |τ|=22 τU(0,0.8 [ 2.0 M G A]) θ A U(0,0.05) θ ̄ D U(0,0.05)
  |τ|=22 τU(0,1.0 [ 2.5 M G A]) θ A U(0,0.05) θ ̄ D U(0,0.05)
  |τ|=22 τU(0,2.0 [ 5.0 M G A]) θ A U(0,0.05) θ ̄ D U(0,0.05)
Uniform |τ|=22 τU(0,0.2 [ 0.5 M G A]) θ A θD1θD2E x p(m e a n=0.025)
  |τ|=22 τU(0,0.4 [ 1.0 M G A]) θ A θD1θD2E x p(m e a n=0.025)
  |τ|=22 τU(0,0.6 [ 1.5 M G A]) θ A θD1θD2E x p(m e a n=0.025)
  |τ|=22 τU(0,0.8 [ 2.0 M G A]) θ A θD1θD2E x p(m e a n=0.025)
  |τ|=22 τU(0,1.0 [ 2.5 M G A]) θ A θD1θD2E x p(m e a n=0.025)
  |τ|=22 τU(0,2.0 [ 5.0 M G A]) θ A θD1θD2E x p(m e a n=0.025)
Exp |τ|=22 τE x p(m e a n=0.058 [ 0.14 M G A]) θ A θD1θD2E x p(m e a n=0.025)
  |τ|=22 τE x p(m e a n=0.115 [ 0.29 M G A]) θ A θD1θD2E x p(m e a n=0.025)
  |τ|=22 τE x p(m e a n=0.173 [ 0.43 M G A]) θ A θD1θD2E x p(m e a n=0.025)
  |τ|=22 τE x p(m e a n=0.231 [ 0.58 M G A]) θ A θD1θD2E x p(m e a n=0.025)
  |τ|=22 τE x p(m e a n=0.289 [ 0.72 M G A]) θ A θD1θD2E x p(m e a n=0.025)
  |τ|=22 τE x p(m e a n=0.577 [ 1.44 M G A]) θ A θD1θD2E x p(m e a n=0.025)
  1. The distributions of divergence times are given in units of 4N C generations followed in brackets by units of millions of generations ago (MGA), with the former converted to the latter assuming a per-site rate of 1 × 10−8 mutations per generation. For all of the msBayes models, the priors for theta parameters are θ A U(0, 0.05) and θD1,θD2 Beta(1, 1)×2× U(0, 0.05. The later is summarized as θ ̄ D U(0, 0.05). For the Uniform and Exp models, θ A ,θD1, and θD2 are independently and exponentially distributed with a mean of 0.025.