Sentence examples for much larger random from inspiring English sources

A future study that derives questions from the categories we have identified would need to be conducted on a much larger, random sample of providers in order to determine generalizability to the population.

We find indeed that in all case the number of communities traversed by each cycle is much larger than random expectation, confirming their role as important features linking together different regions of the collaboration simplex.

To obtain a better approximation of the sampled counterpart model to the real stochastic structure of the demand, it would be desirable to apply variance reduction techniques such as Importance Sampling, and to switch to a variable-sample approach which allows the consideration of a much larger set of random scenarios without compromising runtime too much.

Table 1 shows that for one-channel depleted spots, the imputed value has a mean deviation (md) from the duplicate much larger than expected by random, while the bias for other spots is more comparable to random expectation.

Even though they were randomly selected from a much larger list, a purely random sample could have been more appropriate.

The differences are small, but they may be much larger when the two random effects are not as perfectly cross-classified as in this simulated situation.

Note that the A/ R value for those devices with 10 μm wide contact is much larger than that of random CNT networks (∼10−11), indicating that the noise from aligned CNT networks is larger than randomly oriented CNT networks with the same resistance values (4). 4 shows the measured noise amplitude values (red dots) of the devices with various contact widths.

Having N realizations of K j, we found that their distribution is very far from the true one (the Kolmogorov distances for discrete magnitudes are much larger than those for continuous random variables).

The compositional distances between the UCE and the complete vertebrate genomes appear to be relatively uniform among vertebrates and much larger than those for the random human sample.

It represents the potential modularity of a network and C n of the most real networks is much larger than that of a random networks.

We used ranks instead of numeric importance scores because (Fig.  1) the scores assigned by the information gain are on average much larger than those by the random forest and the GA.