We selected a sample of simulation repetitions to check convergence of the MCMC algorithm and found that all MCMC algorithms converged at the 10000th generation (the standard deviation of split frequencies < 0.0001).
Step 5. Check convergence: the training stage of NTPCA convergence.
Now let us check convergence of the series (13) and (14).
The final model was obtained after ten iterations to check convergence (misfits did not change by more than 0.01 nT between the two last iterations).
The Gibbs sampler algorithm with three simultaneous chains running provide Autocorrelation time series plots of each in different colour help to check convergence.
Therefore, as pointed out in [26], empirical methods are almost always applied, relying on the outputs of MCMC samplers and diagnostics computed from the produced sequence to check convergence.
To check convergence, we adopt the following resolutions: (N_{r}times N_{theta}in {256times128,512times256,1024times512 }) in the 2D cases and (N_{r}times N_{theta}times N_{phi}in{128times64times64, 192times 96times96, 256times128times128, 384times192times192}) in the 3D runs.
All codon models were run at least twice to check convergence.
