Sentence examples for data distributions from from inspiring English sources

The KS test is a nonparametric test used to measure the separability of two data distributions from their cumulative distribution functions (CDF).

This made it possible to assess the deviations of 454 data distributions from the reference genome distribution for the two-, five-, and eight-probe enriched libraries.

This approach made it possible to assess the deviations of 454 data distributions from the theoretical genome distribution for the two-, five-, and eight-probe enriched libraries.

The one-sample Kolmogorov Smirnoff test was used to determine the data distribution from measured variables.

It studies data distribution from samples themselves, that's why we selected this method to present the AUC distributions.

The links between early computer users at the Stanford Artificial Intelligence Laboratory in the 1960s, biologists using local computer networks in the 1970s, and GenBank in the 1980s, show how networking technologies carried particular practices of communication, circulation, and data distribution from computing into biology.

While non-stationaries have a great effect in the S1S2-validation, since the test set has an unknown data distribution, this effect should be minimized when using a crossvalidation (CV), because of the data being permuted and the classifier knowing the data distribution from both sessions.

Data distribution from pyrosequencing analysis was not bimodal and varies among genes; therefore, variables were analyzed as continuous.

The model's parameters were then used to define the data distribution from which to impute the missing values.

PROC UNIVARIATE provided an array of tests (Shapiro-Wilk, Kolmogorov-Smirnov, Cramer-von Mises and Anderson-Darling) for departure of data distribution from normal plus graphic tools (box plot and stem-and-leaf representation) to detect outlying data.

PROC UNIVARIATE provided an array of tests (Shapiro Wilk, Kolmogorov Smirnov, Cramer von Mises and Anderson Darling) for departure of the data distribution from normality, and graphic tools (box plot, stem-and-leaf representation) to detect outlying data.