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While in interpolation, one seeks the value of the field in a location where no sensors are present, imputation approaches try to estimate the value at the sensor location at a time instance where sampling did not take place.
During fitting for the network model, the likelihood function is computed by multiplying the likelihood functions for the 5-h and the 22-h simulations computed as described above, and the fitting procedure seeks the value c that maximized the value of the combined likelihood function.
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Because of the quality of the test data for the 2011 Tohoku Earthquake, we test three values of fault friction (0.0, 0.4, and 0.8) for each source model, seeking the value that produces the highest gain.
Given three integers (plane coefficients) a,b, and c with 0⩽a⩽b⩽c, one can seek the value of ω beyond which the discrete plane P a,b,c,μ,ω) is always connected.
Instead, they seek the value of these resources in the short term from their alternative use, the opportunity cost.
In contrast, an economist will seek the value gained from the resources had they been used for an alternative purpose; this is the opportunity cost.
We seek the value t maximizing this probability, or equivalently, the log-likelihood function, Note that is a sum of log-probabilities log, weighted by their observed counts N ij.
The stability of each periodic state was determined by seeking the values of the corresponding characteristic multipliers.
One step-ahead prediction commonly seeks the estimated state value at the next prediction slot (a =hat {x}).
When used for maximization (seeking the largest value of a fitness function), the fitness surface is imagined as a landscape where higher values (peaks) are better.
We should seek the greatest value of our action.
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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