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State membership of individual probes is determined by the maximum probability criterion.
Thus, input strings cannot be ranked by the maximum probability returned by the Viterbi algorithm in order to find those for which the model is a "poor fit".
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This algorithm starts by finding the maximum probability that may result from selecting each vertex from subgraph (mathcal G_{b}^{1:ell } kappa _{c})).
With the estimated orientation (obtained by selecting the maximum probability index) of each input sample, we cluster the samples which have the same orientation index to get the probability mask and obtain the soft mask from the probability mask through the ungroup operation.
In order to compare across three progressive stages, we standardized PB of the Pre, TvN, and Meta to be within 0∼1 by dividing the maximum probability of each stage.
We first sort the proteins by the magnitude of the maximum probability value (voted from the cells as above) for each protein.
The classification is done by choosing the SC type that has the maximum probability, given by: (5) g = arg max k ∈ 1, 2, 3 π k.
Therefore, the solution is to find the maximum probability by varying two variables: the decay time and the resonance frequency.
Second, haplotypes were inferred from genotyping data by assigning the haplotype with the maximum probability to each case.
We first identified the cytoarchitectonic localization of each statistically significant [ p (FWE) < .05] GMV cluster detected in the mass-univariate analyses by calculating its overlap with the maximum probability maps (MPMs) of each cytoarchitectonic area (Eickhoff et al. 2005, 2006).
For instance, with, one has while, by using the dual-polarized approach, the maximum probability of transmission can be increased up to.
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CEO of Professional Science Editing for Scientists @ prosciediting.com