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The mean vectors, covariance, and the mixture weights parametrize the complete GMM.
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These weights parametrize the scheduling algorithms so each set of weights corresponds to a specific scheduling algorithm.
The GMM is parametrized by the mean vectors, covariance matrices, and mixture weights.
We consider partial identification of finite mixture models in the presence of an observable source of variation in the mixture weights that leaves component distributions unchanged, as is the case in large classes of econometric models.
Consequently, a priori density function is formulated for the mixture weights.
has a shape influenced by the mixture weights.
The mixture weights satisfy the constraint ( {sum}_{mathsf{i}=mathsf{1}}^{mathsf{M}}{omega}_i=1 ).
Most of these discriminative weighting approaches focus on the mixture weights in GMM.
The mixture weights of the components within each state are randomly generated and then normalized.
Likewise, the mixture weights are also recursively updated from frame to frame in the image sequence.
The mixture weights satisfy the constraint that ∑ i = 1 M w i = 1.
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