The choice of energy range and model parameters made here was for didactic reasons rather than to highlight any specific feature in the new database.
Large variations in model parameters make it difficult to predict absolute vibration levels and to make conclusions regarding the relative effects of different designs.
In geodetic data inversion, insufficient observational data and smoothness constraints for model parameters make it difficult to clearly resolve small-scale heterogeneous structures with discontinuous boundaries.
However, too many model parameters make the 2D model unsuitable for detection of range-spread targets, particularly, for detection of high-speed manoeuvring ones in strong noise or clutter.
The draws of the imputation model parameters make parametric imputation 'proper' [ 6] and may be taken parametrically or by the approximate Bayesian bootstrap [ 11].
All these constraints, combined with the complex dynamic of the considered models, can lead to identifiability problems, i.e, the impossibility of unique estimation of the unknown model parameters, making the parameter estimation an even harder optimization task [ 3].
The incidence rates were assumed to be a multiplicative function of the included model parameters, making the logarithm of the rates an additive function of the parameters (Clayton and Schifflers, 1987a, b; Holford, 1991, 1992).
In order to keep general the approach presented here and to maintain the biological reality of model parameters, we make the following basic modeling assumptions: In order to describe quantitatively the protein production (concentration) within the Molecular Biology Dogma, we only consider genes and proteins.
The dependence of the results on model parameters is made explicit through the derivation of an analytic solution.
Quantification of the Mann model parameters is made through wind measurements acquired from the Høvsøre site.
