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Of the 952 individuals analyzed, 735 had a coefficient of membership equal to or higher than 0.70 to their respective identified cluster under the most probable clustering scheme and were retained to investigate the genetic substructure within the 10 clusters mentioned (see below).
Four independent simulations for each K (2 8) were performed to identify the most probable clustering solution through examining the modal distribution of Delta K (Evanno et al. 2005).
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Component memberships of nodes can be estimated in both model variants by the following equation: (8) In the biological experiments we transform these memberships into a crisp clustering by simply assigning each gene to the most probable cluster (component z that maximizes the probability p z| i)).
Predictions of response variables for the test set observations were done both by a) selecting the local model for the most probable cluster and by b) computing the regression coefficients as a probability-weighted sum of the local regression models.
Similarly, if the EM algorithm is being employed, the joint densities are calculated so the test instance is assigned to its most probable cluster.
Strains are assigned to their most probable cluster.
This is done by simply assigning each gene to the most probable cluster.
An alternative more descriptive name for this algorithm is the most probable cluster agglomerative algorithm.
The most probable cluster number was identified using the method proposed by Evanno et al.[ 28].
Bayesian classification schemes of field-collected (wild) males based on shape variables for both mandibles recovered only a single most probable cluster.
The implementation contains two options for prediction: 1) Prediction using the local regression model calibrated in the most probable cluster, and 2) Prediction using a weighted sum of the local regression models, using the estimated cluster membership values as weights.
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