In the EM algorithm for a Gaussian Mixture Model (GMM), what are the E-step and M-step respectively?

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E-step assigns each point to the nearest centroid; M-step recomputes centroids as cluster means.

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E-step computes the posterior probability (responsibility) that each component generated each data point; M-step updates mixture weights, means, and covariances to maximize expected log-likelihood.

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E-step randomly initializes parameters; M-step computes the full posterior distribution over all latent variables.

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E-step updates means and covariances; M-step computes responsibilities.

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In the EM algorithm for a Gaussian Mixture Model (GMM), what are the E-step and ...

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