Sentence examples for unreliable sampling from inspiring English sources

It also suffers from environmental effects, unreliable sampling and poor technical ability on the part of the operatives.

Exact approaches may be considered in study design to overcome the unreliable sample size determination based on traditionally used asymptotic approaches.

The NSF grant will allow researchers to study Asian Americans, who are often ignored in research projects because of their small and statistically unreliable sample sizes in other national and longitudinal surveys, said Ramakrishnan.

The baseline strategy of weight distribution update is to assign lower weights for misclassified or unreliable samples in the training set [20].

Larger cell size means lower cost for sample labeling as the number of cells to be labeled is smaller, but it also means lower classification accuracy as the training set contains more unreliable samples (i.e., higher mislabeled proportion).

The main idea is to combine the current tracking position with the image saliency detection to select the reliable positive and negative samples adaptively to avoid the classifier be harmed by the unreliable sample.

By combining label agreement [17] and label proportion similarity between the predicted and true labels, our reweighting strategy will not only weaken the role of unreliable samples in the training set T but also keep the important role of a certain number of reliable samples within the constraints of label proportions for the major class in each cell.

But it's likely certain operators have avoided fines by submitting unreliable samples.

The CpG units that failed to produce data for more than 30% of samples (unreliable CpG units) and samples lacking more than 30% of their data points (unreliable samples) were discarded [ 31].

The CpG units that failed to produce data from more than 30% of samples (unreliable CpG units) and samples missing more than 30% of the data points (unreliable samples) were discarded [ 29].

Post-PCR analysis was performed to mark failed samples with a Ct value greater than 40 in over 50%% of replicates and to remove unreliable samples with coefficient of variance greater than 30%% in Ct values across replicates.