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Numerical experiments demonstrate competitive accuracy and lower computational cost than expectation-maximization across a range of multidimensional settings.
A fundamental lower bound on the number of samples required to correctly identify the number of components with high probability is proved, showing that this limit depends critically on the separation between component means and the total number of components.
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