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The model settings of the GW simulation were identical to those of the PD simulation, and the experimental design is described in Satoh et al. (2015).
The model settings of validation study were accomplished by comparing the simulation wind field around a single building block to wind tunnel data.
Maximum likelihood analyses were done using the proposed evolutionary model settings of Modeltest with three random addition replicates and heuristic tree search algorithm with tree bisection and reconnection (TBR).
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The work presented here, however, use stochastic and discrete model settings to investigate the various aspects of opinion formation as discussed in subsequent sections of this exposition.
Ultramafic outcrops have long-provided model settings for studies on the ecology of plant species and plant communities.
When comparing the performance of Rega, ANRS and HIVdb within the same model settings, we found evidence of a significantly better performance of HIVdb as compared to ANRS in the univariable Cox (p < 0.0001), multivariable Cox and (p = 0.053), and RSF (p = 0.0005).
We start the evaluation of the model settings by addressing the most prominent question of how many AS signals are we able to identify in the data.
Finally, they allow for a wide range of model settings.
Kodama et al. (2015) documented the model settings and experimental design of the PD simulation.
NH-PhyloBayes [ 63] was used to conduct Bayesian inference with the site-heterogeneous CAT model and the BP option, allowing changes of model settings at "breakpoints" along the tree (empirical optimisation).
Since these model settings represent large classes of dynamic models and because the HC-PLSR method can be adjusted to suite the complexity of the dynamic model behaviour in a very flexible way inviting automation, it qualifies as a promising candidate approach for metamodelling within systems biology.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com