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Using G ˜ and Z ˜, we then obtain our simulated observed genotypes W ˜. This simulation approach does not guarantee that model assumption 1 will hold, because some alleles might not be observed owing either to allelic dropout or to a stochastic failure to be drawn in the simulation.
In contrast, the simulation approach does not have these problems.
This is also illustrated in Table 2. Interestingly, Figure 2 suggests that assuming constant CSHs might be a reasonable assumption for the present control group, but it should be pointed out that the simulation approach does not rely on such an assumption.
Similar(57)
In previous studies, the direct or lagged Pearson correlation was used for identifying such patterns but as demonstrated by our simulations, this approach does not work well for short time-series with strong noise and time-lagged responses (Fig. 4).
However, due to physical limitations of the outdoor testbed (sensitivity threshold, adverse weather conditions, resource sharing, etc), compromises have to be made when experimenting, compared for example to taking a simulation approach that does not take into account those limitations.
In contrast to the PIC simulations, the ideal MHD approach does not include a natural transition to unstable tearing modes.
The simulations confirmed that the empirical approach does not result in an inflated false-positive rate in screens for selection (Table 4).
Our approach does not require numerical simulation efforts.
Our experimental validation of approach does not rely on simulations.
Holland's approach does not include enough explicit simulation engine details to derive time or space performance bounds.
However, this approach does not provide a predictive model that generalizes the simulation data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com