The phrase "a richness of models" is correct and usable in written English.
It can be used when discussing the variety or depth of different models in a particular context, such as in research, design, or analysis.
Example: "The study presented a richness of models that allowed for a comprehensive understanding of the phenomenon."
Alternatives: "a variety of models" or "a wealth of models".
"True richness is not having an abundance of things from the earth, but true richness is having a richness of the soul".
The richness of the models demands a level of visual richness not achievable by painted-on layers or even more sophisticated 3D model layering.
This is a direct result of the richness of the model; as with the example of the acetyl-coA synthetases (above), addition of isoenzymes of specific reactions that do not exist in earlier reconstructions can reduce the predictive power of the model.
Given the richness of our models, we are confident that the variables at hand sufficiently describe migrant characteristics.
Indeed, physical measurement procedures become more accurate when the model underlying them is de-idealized, a process which involves increasing the theoretical richness of the model (Tal 2011).
Given the richness of the model, the variance of the processes being modeled, and the relatively small amount of information in the summary statistics calculated from the sequence data, finding strong posterior support for any scenario is unexpected.
In contrast to these papers, the richness of our model allows a detailed comparison of all the effects of the two frictions, and not just on market competition, but also on welfare.
After reanalyzing sequence data from 22 pairs of taxa from the Philippines [ 7] under the new model, I find a large amount of posterior uncertainty in the number of divergence events shared among the taxa; a result in contrast with the original msBayes model and congruent with intuition given the richness of the model and the relatively small amount of information in the data.
In practice, 2-additivity seems to be the best compromise between low complexity and richness of the model [27].
Such high-dimensional feature spaces contribute to the overall richness of the model, but they can easily lead to high computational cost and to overfitting in later stages.
