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The most previous studies considered the deterministic rules by maximum probability.
Also, Rs-ENG1B is paralogous to some intra-species or intra-genus duplicates from Heterodera and Globodera spp. The Bayesian tree based on the CBM reveals a similar clustering, and grouping of most PPN paralogues is again supported by maximum probability values (eg. Hs-ENG1 and Hg-ENG1; Pc-ENG1 and Pp-ENG1).
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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".
By analogy, we could assess miRNAs by using maximum probability sum columns to select the highest probability of interference with any transcripts.
State membership of individual probes is determined by the maximum probability criterion.
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.
The probability maps of all areas in the ventral visual cortex were visualized as a continuous, non-overlapping map of this region by generating a maximum probability map (MPM).
Therefore, the solution is to find the maximum probability by varying two variables: the decay time and the resonance frequency.
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.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com