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Significance is then tested by the parametric bootstrap approach mentioned earlier.
The validity of the estimated expansion model is tested by the distribution of the sum of square deviations (SSD) between observed and expected values obtained by the parametric bootstrap approach [ 48].
The validity of this model was assessed by the parametric bootstrap approach using the sum of the squares of the deviations between observed and expected mismatch distributions as a statistic as implemented in ARLEQUIN.
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Although we do not know the unbiased estimator, we can estimate the bias by means of the parametric bootstrap.
Confidence in the phylogenetic grouping was assessed by the non parametric bootstrap method [87], [88] with 1000 pseudoreplicates undertaken using the heuristic search option, tree bisection reconnection branch swapping (TBR) and 10 random taxon addition replicates.
The statistical supports for internal branches were estimated by the non parametric bootstrap method on 500 replicates.
Ten thousand replications were obtained by the non parametric bootstrap method.
The robustness of the resulting tree was assessed by the non-parametric bootstrap method (100 replicates of the original dataset) implemented in SeaView.
The robustness of the tree was evaluated by the non-parametric bootstrap procedure implemented in SeaView (1,000 replicates of the original dataset).
As shown in Figure 3, one-dimensional projections of the 2-D 95% confidence regions obtained by a Bayesian analysis were similar to the confidence bounds obtained by the non-parametric Bootstrap approach, shown in Table 1.
The robustness of each branch was estimated by the non-parametric bootstrap procedure implemented in PHYML (100 replicates of the original dataset and the same parameters).
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