Sentence examples for model of randomness from inspiring English sources

A non-parametric model of randomness is employed in the SEA components, based on diffuse wave theory and the Gaussian Orthogonal Ensemble (GOE), and this enables the mean and variance of second order quantities such as vibrational energy and response cross-spectra to be predicted.

The ARI computes the fraction of all possible pairs of voxels that are either i) in the same region in both parcellations or ii) in different regions in both parcellations, and is normalized such that its expected value is 0 under the generalized hypergeometric model of randomness.

Beside bootstrapping, popular tools frequently used by systematists were for some time the estimation of consistency and of tree length distribution under the parsimony criterion [ 104- 108] and derived tests that measure departure from a model of randomness.

These results suggest that (i) analyses of 1D data based on 0D models of randomness are generally biased unless one explicitly identifies 0D variables before the experiment, and (ii) parametric and non-parametric 1D hypothesis testing provide an unambiguous framework for analysis when one׳s hypothesis explicitly or implicitly pertains to whole 1D trajectories.

He also looks at finance theory that is somewhat beyond the mainstream chaos theorist Benoit Mandelbrot's model of "wild" randomness.

From the simulation we can see that the channel models are of randomness and multipath effect, so that the difference result corresponding to the right timing index is not always the maximum one.

I've long been cautious about any model employing hypotheses of randomness in model components, particularly the backbone method of multiple linear regression, popular accounts of which may have encouraged the use of such hypotheses elsewhere.

Optimum parameter values yielding maximum randomness are calculated using a mathematical model of true random number generator.

The simulation's stochastic nature, caused by various elements of model randomness leads to varying response averages amongst simulation runs.

Outcomes in a deterministic model can be predicted completely if the independent variables (input values) and the initial state of the model are known; whereas, stochastic models introduce a degree of randomness or chance into a possible set of outcomes.

We call it a semi-deterministic model because it is mainly based on the deterministic MR-FDPF model, but it also introduces a random part to model the randomness of realistic radio channels.