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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.
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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).
The 10 most probable nonoverlapping clusters of leprosy (Table 1) were located between latitudes 21°S and 4°N.
The most probable number of clusters identified by TESS Bayesian cluster analysis was four (K = 4).
The goal is to find most probable mapping of cluster i at time t to a globally consistent index k.
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