Sentence examples for a general error from inspiring English sources

However, we do not intend to focus on a general error rate, but rather assess the effect of several variables on error generation.

A Blizzard rep managed to show up and copy and paste a general error message.

Here, a method for inverting contaminant transport models is presented including a general error model and procedures for differentially weighted multiple response regression.

In this note we derive the rate of divergence of the penalty term for consistent model selection in the linear regression model under a general error structure.

Next the outputs of the cable tension and bending moment of the proposed method and the finite element model are presented in Table 5, in which it is possible to observer a general error below 4.3%, which leads to the conclusion that even on a 3D model with geometrical non-linear analysis and tower flexibility, the proposed method possesses extraordinary results for the pre-design phase.

Here, we describe GemSIM - a General, Error Model based SIMulator of NGS sequencing data.

ACC has been found to register errors both when they are detected by the individual and when external error-feedback is provided and is thus regarded as a general error-monitoring module (Holroyd et al., 2004; Ullsperger, Nittono, & von Cramon, 2007).

Testing examiner accuracy using known samples can give the judge or jury a sense of general error rates in a field, but it can't describe the level of uncertainty around a specific piece of evidence.

Low correlation and high average strength result in a reduction of general error bound measured by c/s2 which indicates a high accuracy RF model.

We applied a probabilistic approach to construct a network with less bias against membrane proteins by using a simple and general error model based on the hypergeometric distribution to calculate the probability for each interaction occurring at random [ 32].

We used a simple and general error model (based on the hypergeometric distribution) to calculate the probability for each interaction occurring at random, followed by logistic regression to optimize the network performance for interactions involving membrane proteins by varying the weights of each network.