Sentence examples for approaches for random from inspiring English sources

Therefore, we explored possible solutions and finally propose two different approaches for random number generation.

As random number generation can be computationally time consuming, and the software requires a large amount of random numbers, Meredys gives the user a choice of two approaches for random number generation.

One of the most promising technologies for characterizing all of the TE-based variation with minimal ascertainment bias is the potential usage of some of the upcoming next-generation DNA sequencing approaches for random sequencing of the entire genome of an individual.

Parizi et al. [22] presented an automated approach for random test case generation and uses mutation testing as a way of assessing their approach.

To correctly incorporate stress variability in the increasingly widespread application of probabilistic‑related rock mechanics analyses, a robust approach for random stress tensor generation is essential.

Meta-analysis was implemented to combine the individual association results for 2,925,090 imputed and genotyped SNPs (under additive genetic models) available in all three GWAS using the inverse-variance approach for random effect models.

Both algorithms are shown to outperform simple heuristic-based rate allocation approaches for numerous random network topologies.

Obviously, this is a major improvement over existing approaches for the random generation of secondary structures of a given input size n (where the corresponding specific RNA sequence is not known, but only its length n), as those (sequence-independent) methods are only capable of generating structures uniformly at random for input size n.

Knowledge of this impingement function allows treating non-random nucleation sites in a manner that parallels the approach used for random sites.

This difference-based approach compensates for random cell culturing artefacts and should identify the regions most strongly linked to clinical disease.

Here, we propose a multivariate random vector generation approach for generating random stress tensor components that is based on tensorial techniques and which incorporates inter-component correlation.