Sentence examples for model outputs as from inspiring English sources
Thus, the conceptual framework follows the typical steps of conducting a model-based ESS: collection and preparation of empirical data for the model-based data processing, assumption-making, model application and preparation of model outputs as well as deriving comprehensive conclusions and recommendations.
When an outbreak was not detected by syndromic surveillance, the detection benefit was 0. For a given release scenario, each of the 1,000 simulations integrated both randomness in the component model outputs as well as uncertainty in component model parameters.
Visualizing the model outputs as mapped information (called VegPredict) provides a means to evaluate the model.
Stakeholders may question the very purpose of trying to say anything meaningful about such a distant future; may consider model outputs as irrelevant to planning; or may provide emotional, often unconscious, responses motivated by deeply held fears and aspirations.
To further investigate the uncertainties associated with the empirical model outputs as a function of height, a linear fitting is applied between Ey and Ez inferred at the four different radar range heights (Fig. 1) and the corresponding component inferred from the 12 simulations (Ey U/O and Ez U/O), respectively.
We expressed model outputs as probability distributions summarized by a mean point estimate with 90% credible intervals (CrIs).
The standardized MMP values correspond with the values of the GRNN model output as described below.
Runs, a series of tests, are designed in order to carefully asses the response (model output) as a function of changes in input variables.
Indeed, a slightly higher false positive rate could be considered an advantage when using the model output as a compound triage tool for deleterious safety effects.
It utilizes high-dimensional model representation (HDMR) technique in the stochastic space to represent the model output as a finite hierarchical correlated function expansion in terms of the stochastic inputs starting from lower-order to higher-order component functions.
As a very practical way the following identification strategy was used: independent model parameter estimation in the different working points and the calculation of the global valid model output as the weighted sum of the sub-models.
