Our focus is on the conceptualization of longitudinal statistical models, the assumptions associated with them, and the interpretations of model parameters.
As a result, interpretations of model outputs were more measured and cautious.
Demographic modelling assumptions should be considered in the interpretation of models of chronic infectious diseases such as TB.
Hence, interpretation of models, including nonlinear terms, must be approached with care and re-interpreted as model terms cycle in or out; standard model term selection metrics can be misleading without visual assessment and confirmation.
Hansen et al. have developed a method to allow for the interpretation of models built using kernel based learning algorithms.
An alternative approach for model execution is the interpretation of models directly without converting them into an HLL.
To enhance the interpretation of models involving richness, distance-based redundancy analyses of species composition data were performed.
It also leads to more reliable interpretation of models across the product lifecycle due to more rigorous language semantics.
Thus, the structural interpretation of models specified in formalisms such as SBML may vary according to the source of the original model.
Interpretation of models directed at interaction also needs to consider error in the measurement of the pollutants and the consequences of errors that are differential among the pollutants.
