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The chains were thinned by only collecting 1 in 10 consecutive samples and this eliminated autocorrelation in posterior samples (using the CODA package [ 26] in R).
The samples were thinned by only collecting one in 10 consecutive samples to eliminate autocorrelation in posterior samples (the R package "coda" [ 27] was used), so that in total we ended up with 20,000 samples.
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We then extended the miR-10 analysis to also include qRT-PCR analysis of tissue samples dissected, including more caudal levels of the neuroaxis, and found that miR-10a and miR-10b expression is gradually increased in more posterior samples, reaching high levels in the SC (Fig. 5F).
The MCMC analysis was run for 100000 generations with chain sampled every 100th generation resulting in 10000 posterior samples.
The posterior inclusion probability for each locus is estimated as its frequency in the posterior samples.
The MF% column shows the mean percentile of the summary tree log-likelihood (tree+coalescent) in the posterior samples.
In addition, posterior samples from different (five) runs were compared, which is arguably the best approach to detect potential problems of chain convergence [ 44].
200k of the initial samples were discarded (burn-in), leaving 1000 posterior samples.
Branch robustness was assessed through their posterior probabilities (i.e., the proportion of trees in the posterior sample in which the considered branches appeared).
We ran our Gibbs sampler for 12,000 iterations, discarded the first 10,000 samples as burn-in, and collected 1000 posterior samples with a thinning rate of two.
Results in terms of posterior samples allow for direct probabilistic statements about model parameters, which may be also used in other analyses such as simulation models for testing management strategies.
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