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Consequently, a priori density function is formulated for the 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.
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 Student t distribution for mixture weights was introduced to achieve sparse basis representation.
The mean vectors, covariance, and the mixture weights parametrize the complete GMM.
The GMM is parametrized by the mean vectors, covariance matrices, and mixture weights.
The mixture weights of the components within each state are randomly generated and then normalized.
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