Sentence examples for generalized over from inspiring English sources

In contrast, the predictions of biophysical and ecophysiological models cannot be generalized over more than one generation if the microevolution of functional traits within a population is not negligible.

To create the artificial population, a large multivariate dataset containing NFI field observations and ALS metrics was generated using a copula function fitted to the empirical observations, and then it was generalized over the study area using satellite imagery and nearest neighbor imputations.

Because interpretations are generalized over, the actual interpretation of 'K' plays no role.

Carrying out an extensive in silico study, the experimentally observed advantage could also be generalized over a statistically significant amount of different 10 and 20 component samples.

The main advantages of this method are that it enables a systematic design approach, and (unlike most nonlinear observer design methods), it can be generalized over a larger class of nonlinear systems.

Bimanual coordination dynamics have been conceived as the outcome of a global coordinative system, and coordination stability properties and theories of underlying processes have often been generalized over various bimanual tasks.

The question now remains: do the more abstract points on the cline omit any (irrelevant) information that is found in their concrete counterparts, i.e. the set of texts that they are statistically generalizing over?

We could further take advantage of abstraction by incorporating ontologies such as the Cell Ontology (CL) in addition to ChEBI, or by generalizing over linguistic forms, such as abstracting words like 'regulation' and 'proliferation' to - ION_ WORD.

For the D-Study, a universe of admissible generalizations, which represents the measurement conditions based on the object of measurement and the measurement facets a researcher is willing to generalize over, is defined and the variance components associated with the universe of admissible generalizations are estimated.

Finally, this can be generalized to over-dispersed Poisson shapes by introducing a single over-dispersion shape parameter s to allow distributions with excess variance: P NO x ; c, a n, s = ∑ n a n NegBinomial x ; mean = c * n ; alpha = s / n.

When this parameter is near zero, the number of categories will approach one, thus over generalizing over the training set.