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The same general approach to computing the scores for 2015 are still employed and are discussed in detail above.
Using the square-root rule helps compress the variations of the number of templates k i used in computing the scores, since the number often vary largely in different triphone clusters.
So after computing the scores for all tuples of size c, we can get rough estimates of how many guilty users are contained in each tuple, and for instance, try to find the set (mathcal {C}') of c users that best matches these estimates.
Finally, computing the scores of all blocks B i, j with their sizes bounded by r takes O(mr).
In computing the scores in Equation 2, S ranges over all the nodes in the tree traversal except the root.
Resolving the recurrences: Now, it is time for our final trick: instead of computing the scores using recurrence (6 8), we apply a successive approximation procedure similar to Dijkstra's Algorithm for shortest paths (Sniedovich, 2006).
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The algorithm for computing the score (or 'tariff', as referred to by some) is straightforward.
We have selected JTT evolutionary substitution model for amino acid replacements and Bayesian method for computing the score.
We present several methods to compute the scores both for the entities and for the terms in the search results.
In such a concurrent postings traversal we compute the scores of one document at a time, so that it is sometimes called document-at-a-time scoring.
The elements of the eigenvectors that are used to compute the scores of the observations are called principal component loadings.
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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