The results show that the revenue is increased under large value of blocking probability constraints and spectrum demand.
This further causes the inferred edges under some large values of K to have similar results to the ones under a smaller value of K. C-CENI, on the other hand, has a different story.
The numerical results indicate that, under the Zipf model under consideration, and for moderate and large values of the actual CoD, the resubstitution CoD estimator is the least biased and least variable among all CoD estimators, especially at small number of predictors.
The numerical results lead to a perhaps surprising conclusion: under the Zipf model under consideration, and for moderate and large values of the actual CoD, the resubstitution CoD estimator is the least biased and least variable among all CoD estimators, especially at small number of predictors.
While the interference is fairly mild for large values of N under the parameters of Macpherson et al. (2007), the amount of interference is extreme at all values of N under the Jensen et al. (2008) parameters.
In contrast, under CAT, the n-step homoplasy probability is always higher than under WAG, and remains high (at around 0.32) even for large values of n.
This may explain some particularly large values of the CBO that have been measured under flat-band conditions.
That is, the profiles for large values of f C become widely distributed, with a master sequence that seems under-represented.
Large values of R λ and η represent a high sensitivity of the device under specific light illumination.
Bottom: large values of σ.
