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Simulations show that numerical improvements are acquired in light of a higher percentage of convergence, and smaller standard errors and mean square errors of parameter estimates.
The results are summarized in Figure showing the percentage of convergence to the global minimum, local minima or failure for different box sizes.
The results are given in Figure a showing the percentage of convergence to the global minimum, local minima or failure of Box 5, Box 10, and Box 100, respectively.
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The study further brings out the influence of mutation percentage on convergence.
This additional mechanism leads to a noticeable improvement of convergence times (Fig. 6c) and a much higher percentage of convergent cases (Fig. 6d).
By the experiments and analysis under the contamination, we can observe that: (1) do not consider the intensity of the contamination, the bigger the number of looks, the smaller the percentage of no convergence.
The MDI cut-off score of >25 for major depression had a percentage convergence of 80 % with the HAM-D17 score of >18.
When using the conventional HAM-D17 cut-off score of 18 for major depression and the MDI cut-off score of > 25, we found that within the 826 observations (Table 2) the percentage convergence of MDI was 156 out of 195 observations with HAM-D17, or 80.0%%, i.e. an acceptable convergence, but of moderate degree.
It is seen from Figure 5 that as the percentage of informed bees increases, the convergence speed increases and the MSE decreases.
The convergence in percentage of the gene across the three analysis methods was calculated.
As is usual in philosophy, we can't be very precise about the percentages, but we suspect that the relevant notion of convergence – always remembering that we are discussing ideal conditions – should be thought to allow only minimal dissent.
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