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The largest log likelihood ratio for our simulated data sets was 5.506 and the likelihood ratio for over 96% of our simulations was less than 0.001, whereas the likelihood ratio for our actual data was 18.064.
The parallel shift of graphene requires less external power than the perpendicular shift for our simulated systems.
The remainder of this paper is organized as follows: section 2 presents the system model used for our simulated environment.
This configuration was found to be the best configuration using discrete exhaustive search method for our simulated scenario [22, 23].
For example, Figure 6 shows the range profiles for our simulated example with a radial translational motion aligned after applying the peak tracking method.
The gravitational effects for our simulated reservoir were witnessed after 15 years of production and were measurable due to consistent conditions assumed throughout production time.
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For two of our simulated scenarios (scenarios 3 and 5), the one-step PC and one-step GMRE analyses were more powerful than the two-step analyses.
This recursive algorithm is fast in practice and runs in less than a minute, on a single CPU, for each of our simulated or real samples.
We computed the AIC instead of the AICc for given models, in that the sample size n (i.e., the sequence length) for most of our simulated datasets was large enough compared with the number of parameters (K) [ 40, 58].
By setting the type I error rate at 0.05, we would just by chance expect to infer N A, N 0, and τ incorrectly for ~5% of our simulated datasets.
For example, in our simulated data set of 10,000 participants, a realistic sample size for large-scale genetic association studies, obtaining exposure data for approximately 20% of the full sample achieves maximum power when the first-stage R is greater than 0.015.
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