Sentence examples for samples for computing from inspiring English sources

The method is based on an analytical property of the so-called design point vector; this property is exploited by constructing a nonlinear projection of Monte Carlo samples of the input variables in a two-dimensional diagram from which the analyst can easily extract the relevant samples for computing both the lower and upper bounds of the failure probability using random set theory.

Totally 300 snapshots are run for collecting the random samples for computing RMSEEs.

Where S is the total samples used for computing power, P M,1 represents the power of signal and P M,0 is the powerof noise.

Then, we derive a sampling algorithm for computing the posterior distribution over latent variables in the model and use those samples in a Bayesian posterior distribution hypothesis test to call variants.

An analytical study of the failure region of the first excursion reliability problem for linear dynamical systems subjected to Gaussian white noise excitation is carried out with a view to constructing a suitable importance sampling density for computing the first excursion failure probability.

The seventh item of the SWB subscale, GS7 asking about the sex life of the patient, introduced a non-response rate of 48% (n = 32), which greatly reduced the sample size for computing the reliability index.

The sample size was calculated using three scenarios for computing sample size: (1) using the prevalence of knowledge about menstruation at 92% [ 12], (2) the prevalence of sanitary napkins use at 37.6% [ 12] and (3) the prevalence of school-absenteeism at 17% [ 13].

Monte Carlo estimators are derived for all the developed sensitivity concepts based on the n+2 samples matrices originally used for computing the main and total effect indices, thus no extra computational cost is introduced.

However, the assumption that is often made is that the data samples are large enough for computing the statistics of interest [39].

The clients have one-to-one correspondence to the given samples, each client being responsible for computing the frequencies within the designated sample.

Bayes success run theorem appeared to be the most appropriate approach among various methods considered in this work for computing sample size for PPQ.