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Grid points within object A are given the value s and the exterior is set to Oa; the corresponding grid values for object B are set to s and Ob, respectively.
The 21-km grid values for this metric were then assigned to the 6-digit postal codes of the subjects in our cohort.
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By this method it is possible to obtain the value of interest by direct interpolation between the sampled values obtained with the same set of random numbers for the grid values of the parameters adjacent to the values of interest.
Different grid values are used for each household.
We used the systematic sampling grid values as the training dataset for each random forest model.
This may enlarge weights for far-away grid values as shown in (3.19), which in return acts as a force to reduce function values.
Moreover, we have considered a grid of values for p on the interval from 1 to 3. Further, for each simulated dataset we have used k=2, x i =(1,x1i∼N 2,1∼N(2,1), β=(1.5,−3), and σ=1.
These data are based on interpolations of surface observations, which provide a grid of values for 5 km squares across the UK.
Parameters are estimated using a maximum likelihood approach, maximizing the likelihood over a two dimensional grid of values for (f,μ).
We simulated sequence data for each population across a grid of values for τ and α, including constant size (i.e., τ = α = 0), using the n-coalescent model implemented in ms [46].
Typically, we first pick a grid of values for λ2, say For each λ γ, 2, the RENET-VAR algorithm produces.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com