Sentence examples for ideal for modelling from inspiring English sources

Because of the uncertainty, our data is not ideal for modelling herd growth with assumed rates of births, deaths and offtake (see Dahl and Hjort 1976; Oba 2001 for the assumptions made in such models).

While having 2 replicates for each time-point/fragment is not ideal for modelling the variance encountered in RNA-seq, our sample preparation of including multiple individuals (20 fragments in each library) does offer some variance stabilization through biological averaging.

On the other hand, BioASP only calculates predictions under repair that hold true under all repair sets (i.e. not all possible), however it is capable of dealing problems of much larger size, rendering it ideal for modelling large scale regulatory networks.

27 These experimental results are ideal for modelling purposes, since the data show a wide range and spread of activity values.

Also, stereotyping seems to be ideal for modeling students' learning styles and preferences.

The PDF characterized by (9) is demonstrated as an empirical model which is ideal for modeling ultrasound amplitude RF returns.

hNSCs are multipotent cells that primarily differentiate into neurons, oligodendrocytes, and astrocytes, making them ideal for modeling neural regeneration.

While the CDE provides a good basis for modeling private consumption across a broad range of households and countries, it is not ideal for modeling extreme situations, where poverty and subsistence expenditures are dominant.

Nonparametric classification algorithms such as Random Forests are also ideal for modeling complex, non-linear relationships and avoiding problems of autocorrelation and unknown variable interaction across spatial and temporal scales ([27], [29]).

Furthermore, we observed that the Gaussian mixture model and the parameter selection used in VQSR are not ideal for modeling these features and proposed to use a boosting method that uses generalized linear models as its base learners.

We note that while the Gaussian assumption carries the benefits of mathematical tractability for the derivations that follow, it is not ideal for modeling a distribution over a random variable confined to a finite range.