Sentence examples for evolution of statistics from inspiring English sources

The time evolution of statistics such as turbulent kinetic energy (TKE) and enstrophy are shown for various combinations of modified Roe-MUSCL and temporal schemes at 643 and 1283 grid resolutions.

The full model and the limiting model for γ=0 are stochastic, and the limiting model approximates the evolution of statistics of the species numbers quite precisely.

The proposed BSU approach is shown to perform satisfactorily and provide insights into evolution of the statistics of geotechnical parameters as more results of different testing procedures are used.

Figure 7a, b shows the evolution of the statistics point of enhanced image as parameter mmin increasing from 40 to 100 with fixed parameters (Sigma, mmax) = (16, 150) and (Sigma, mmax) = (16, 250), respectively.

By studying the evolution of region statistics, this unsupervised method highlights nodes which represent the boundary between salient details and provide a set of tree levels from which segmentations can be derived.

Below is a summary of new ideas presented by the breakout groups: Critical issues in the teaching of evolution Student understanding of statistics and probability, which is required for understanding the underlying data that describe evolutionary processes.

In order to detect non-neutral patterns of evolution, summary statistics for the R-genes were compared to a genome-wide sample of 'reference' genes from plants of both species from related plant families of equivalent geographic sampling [ 5], each containing a minimum of 60 synonymous sites and a sample size of six.

This process yields a first order differential equation governing the time evolution of the filter response statistics.

The study provides (i) the evolution of multi-fluid probability statistics as a function of the Damköhler number, including (ii) the flow direction across fluid interfaces and OH gradients, (iii) mean flow field statistics, (iv) conditional velocity statistics and (v) a tentative combustion regime classification.

For a given model parameterisation, the Gillespie algorithm is used to simulate an ensemble of 10 possible realisations of the time evolution of mtDNA content, and the statistics of this ensemble are recorded.

The co-evolution of the different populations is controlled at the level of the union of populations, i.e. the metapopulation, on the basis of statistics and trends of the evolution of every population.