We also present a procedure to construct a well-fitted profile by merging any redundant model components in a way that minimizes change in entropy of the resulting model.
On the basis of the above analysis a variant of Sirius was developed in which all the identified redundant model variables were reduced with the aim of comparing the resultant predictions with those of the full model.
To this end, we used two models (see statistical analyses): model 1 included all variables listed in Table 1 and was therefore highly redundant; model 2 included, at each step, only variables selected by a stepwise multiple regression procedure (IFG: none; IGT: age; newly diagnosed diabetes: plasma glucose; 0 9 years: age and A1C; 10 19 years: age; ≥20 years: age and A1C).
Numerical results are included based on laboratory data to show typical steps of a model validation procedure, i.e., the interpretation of confidence intervals, the compliance with physical laws, the identification and elimination of redundant model parameters, the computation of optimum experimental designs and the identifiability of parameters by determining weight distributions.
Following re-parameterisation of the Niederer-model, we apply the same methodology for finding reduced model versions through the identification of redundant model components.
This was carried out to test whether the Niederer-model parameters could be predicted directly from the Land-model outputs using regression, and to identify redundant model components for both models.
Using our approach, different model alternatives can be compared, allowing effective analysis of the consequences of introducing changes to the models and identification of redundant model components that can be omitted without affecting the fit to measured data.
We therefore believe that this approach has significant potential for reparameterising existing frameworks, for identification of redundant model components of large biophysical models and to increase their predictive capacity.
In this sense, in the phylogenetic analysis, there is a potentially (partially) redundant modeling of mutational features, in having explicit parameters devoted to these, in combination with the use of the SC setting.
According to computer simulations using a genetic redundancy model [ 5], redundant genes do not persist when they are equally effective at performing their functions (Model 1).
