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Mathematical modeling could benefit greatly from the deluge of data emerging from metagenomic studies, but data-driven approaches such as network inference that aim to predict microbiome dynamics without explicit mechanistic knowledge seem better suited to model these data.
A normal distribution with a log link function is chosen to model these data.
So, the PCNB distribution can be used effectively to model these data.
So, the OLLLW distribution would be a good option to model these data.
The plot in Fig. 8b reveals that the third data set has UBT failure rate shape, and then the PCNB distribution can be used effectively to model these data.
In the case of the zonal model, these data are required for the population of each zone.
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Modeling these data was based on a non-electrostatic approach.
Contrary to lower-resolution data currently used for ice-sheet models, these data show a contrasting topography across the ice-bed interface.
Modeling these data begins with the Poisson regression model has been used for several years.
An alternative approach for modeling these data can be provided by the LW distribution.
Hence when inversely modeling these data, G increases but this has nothing to do with the change in tissue properties [66].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com