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To make the problem numerically solvable, we discretized the neuronal distribution; in both models, we divided the preferred spatial frequencies into 50 bins that are evenly arrayed in log scale and varied the number of neurons that prefer spatial frequencies within each bin as a parameter.
To estimate these models, we divided each coverage profile according to the respective GC content and analysed the corresponding data separately.
On the basis of mapping to Amel_2.0 and overlap between the OGSv3.2 coding sequence alignments with OGSv1.0 gene models, we divided the 15,314 Ogenes2 genes into three sub-sets (Table 5).
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In order to make the SSA more efficient for large models, we divide large inverted images into several subsections by small spatial windows.
To quantitatively assess the complexity of the models
In our model, we divided the type of coal and gas outbursts into four grades regarded as four normal populations.
For RPGM model, we divided all nodes into four groups, there are 25 nodes in each group.
Using a hierarchical scene model, we divided 30-m resolution LAI and NDVI images from Maun (Botswana), Harvard Forest USAandand Ruokulahti Forest (Finland) into individual scale images of classes, region and pixel.
In order to confirm that this is the case in the Bayesian model, we divided the vocabulary into two subsets according to the density of their neighbors.
In the model we divided the vaccination scheme into three parts: (i) the vaccinations at 2, 3, 4 and 11 months of age; (ii) the booster dose at 4 years of age; and (iii) a booster in adolescents at 12 years of age.
Before building the prediction model, we divided 5,203 entries into 2 sets, the training set and the testing set.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com