Sentence examples for model outputs such from inspiring English sources

When underlying data lack clear indications of associated uncertainty, modellers often fail to account for that uncertainty in model outputs, such as estimates of population growth.

Their efficacy was assessed on model outputs such as the prevalence and probability of extinction at the metapopulation level.

The sensitivity of the input parameters for the model directly indicates their close relationships with the model outputs such as the residual flux, exchange volume, residual salinity and residence time (Gordon Jr. et al. 1996).

Uncertainty in model parameters was quantified using confidence intervals obtained through bootstrapping the data, and uncertainty in model outputs such as the predicted degree distribution was quantified using a parametric bootstrap.

Model outputs such as the DTES fatal overdose rate and non-fatal to fatal overdose ratio are not substantially different from previous observations in other settings [31], lending credence to the estimate of averted deaths.

Such values can be estimated by calibrating the model, that is, adjusting its internal parameters until model outputs (such as cancer incidence) match observational data.

This approach is also advantageous in that it yields an appropriately calibrated posterior distribution for all model parameters, and therefore all model output, such as incidence and antiretroviral therapy (ART) coverage.

For example, the sensitivity (S) of an input parameter (Pi), such as KOW, on model output, such as iF, can be approximated as S = (Δ iF/ iF /(Δ P/ Pi), [1] where Δ iF is the change in the iF value and Δ P is a fixed change to a selected input parameter value (e.g., 0.1% change in KOW).

Model outputs for such regions (see figures S1, S3 and S4 in the online methodology supplement) show that the coefficients for the fixed effects of the FAO diet composition components (especially component 1) are larger than the coefficients for income (which were expected to be low).

In this manner, we built surfaces of model outputs of interest (such as slope of mutation accumulation or magnitude of clonal expansions over lifetime) within the effective range of mutation DFE variance and mutation rate increase.

However, this method may lead to unreliable results and the Hosmer-Lemeshov test (developed for logistic-regression models) may not be appropriate for models with discrete outputs such as scoring systems [ 13].