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The MCMC was run for 180 000 iterations, with a burn-in of 20 000 iterations and a thin interval of 50.
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Three independent chains of MCMC were run with 40 000 thinned updates and a thinning interval of 20 000 steps (i.e. total number of update steps = 8 × 10).
The posterior distributions of the different parameters of interest were estimated via MCMC procedures as previously described [13] from 2,000 post burn-in samples (with a burn-in period of 2,500 iterations) and a thinning interval of k = 25.
We used a burn-in of 50 000 iterations and a thinning interval of 10.
For each gene, we run 10,000 iterations with a thinning interval of 10 iterations.
Default values were used, which means 100 iterations, a thinning interval of 1 and a burn-in of 100.
The algorithm was run for 1000 generations with a thinning interval of 1 and burn-in of 100 generations.
Length of the MCMC was set to 20,000,000 iterations with a 10% burnin and a thinning interval of 1,000.
Each short run had a total of 250 000 iterations with a thinning interval of 20 including a burn in of 50 000.
Preliminary tests were conducted using a burn-in of 10,000 iterations, a thinning interval of 50, and a sample size of 10,000 (Foll and Gaggiotti 2008).
Each long run had a total of 2 050 000 iterations with a thinning interval of 20 including a burn in of 50 000.
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