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We describe EDM, a value calculated directly from a penetrance function and it's respective genotype frequencies, for the purpose of predicting model difficulty.
We demonstrate how the combination of the GAMETES model generation strategy with a model difficulty metric allows for the intelligent, automated selection of model architectures for simulation studies.
Together, the findings presented above suggest that either EDM or COR would serve as a suitable metric with which to gauge model difficulty in place of heritability alone.
A two-parameter model (difficulty and discrimination) has not been employed to assess the temporal stability of performance based examinations such as the OSCE.
While the model difficulty metrics considered in this paper could be applied to any penetrance-function-based model, we examine models generated using GAMETES.
Since GAMETES selects models representative of this model difficulty range, maximizing this range encourages the selection of best models to represent the easiest and most challenging models based on model architecture alone.
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Modeling this included difficulties in comparing user and expert models, difficulties in setting preconditions and initial conditions and modeling aspects that did not need to be modeled.
However, modeling difficulties make the plant dynamic model poorly defined.
Modeling difficulties may also be encountered close to the rupture where model discretization impacts the computed stress changes.
The complexity, scale and uncertainty of natural systems are the primary reasons for most of the modeling difficulties which have to be handled.
Agent-based thinking challenges They represented difficulties students faced in expressing agent behaviors as computational models, difficulties in understanding how individual agent interactions lead to aggregate-level behaviors, and the consequences of agent behavior changes on the aggregate behavior.
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