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"We could go through a modeling exercise where we make all sorts of different assumptions and make predictions, but that's really more than I think we can expect our staff to do".
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SERs must make assumptions or undertake modeling exercises where prevalence is unknown.
As with any modeling exercise, our model was constructed under the assumption the system would not change.
It is important that, along with stating the objective of the modeling exercise, the model evaluation procedure is also justified up-front.
Thus, it is appropriate to start a modeling exercise with a simple model (with few variables) and then add complexity as needed.
In this sense, FIRM is a parsimonious model and does not reach this level of granularity, although the model structure is amenable to be extended and incorporate such considerations where warranted, required by the purpose of the modeling exercise and supported by the data.
This approach presents a formal modeling exercise for using spatially explicit, individual-based models to conduct population viability analysis.
Optimization of the number of neurons for further modeling exercise is one of the most crucial part of any ANN model development.
The parameters from the modeling exercise are validated using a hold-out sample not considered for model estimation.
One such modeling exercise is that of Fernandez de Cordoba and Kehoe (2000).
The success of that modeling exercise is based on the specification of selected assumptions about learning in industry.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com