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In addition, these methods generally perform best for problems involving convex or 1-d quasi convex feasible regions with respect to the uncertain variables.
In scenario 2, all methods generally perform more equally regarding efficiency, although method 4 still performs better than the others.
The results in the Supporting Information show that all three methods generally perform very similarly on average.
The multi-model quality prediction methods generally perform better than the single-model quality prediction methods given the pool of models is sampled by independent structure predictors.
Our tests reproduced the earlier observation that hybrid methods generally perform better than standalone global methods (Ashyraliyev et al., 2009; Rodriguez-Fernandez et al., 2006).
While different methods generally perform well when there are a large number of levels of each factor, large differences between the methods emerge when the number of one or more factors is limited.
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The alignment-free methods generally performed worse than other methods tested on the 18 taxa tree but were close to the best alignment-based methods for the 52 taxa trees.
Through a simulation study, they found that the three BayesT methods generally performed better than the corresponding standard Bayesian methods, especially when only two phenotypic categories are present.
In all these cases it was observed that the first method generally performed better than the second.
Through the numerical studies we find that our SPLS method generally performs better than the standard PLS and other existing methods in variable selection and prediction.
Since this method generally performs well and is one of the simplest, it is probably the most favoured of the non-tree-structured methods.
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