Sentence examples for making robust predictions from inspiring English sources

A considerable challenge to quantifying this extinction risk is integrating robust estimates of dispersal rates (necessary for understanding mechanisms) with detailed data on local demographic rates (necessary for making robust predictions of how dispersal affects local population dynamics).

At least part of the explanation for this reactive role lies in the widely recognised difficulty of making robust predictions about medicolegal risk at the individual clinician level.

Using four example data sets, each with its own specificity, we provide evidence that this methodology is successful in making robust predictions of non-independently evolving positions.

I will show how these can be used to make robust predictions of the 21cm signal from the early Universe, focusing on the pre-reionization epochs.

Without necessarily understanding all the local decision criteria used by any individual, we can make robust predictions about how a group as a whole will interact.

This investigation indicated that an accurate and complete modeling approach is required to make robust predictions in the processing of the experimental data.

By assuming that they operate during navigation, we can make robust predictions.

If ecologists wish to build parsimonious GLM models that will allow them to make robust predictions, a reasonable approach is to use a large number of randomly selected pseudo-absences, and perform model selection based on an information theoretic approach.

Ultimately, we combined the biophysical model for the OSN spiking dynamics with the GLM to make robust predictions about closed-loop behavior in virtual and in real odor gradients.

In addition, we implicitly assume that current sexual behaviour will remain roughly unchanged, but this is done in all modelling studies we are aware of as it is not possible to make robust predictions into the future.

Such a situation is quite normal in biological data analysis [ 42- 45], when models can make robust predictions with regards to the behavior of the studied system (in our case the distribution of isotopologues), but such prediction remains valid for various sets of parameters (fluxes).