The EM algorithm finds maximum-likelihood estimates for model parameters when you have incomplete data. The “E-Step” finds probabilities for the assignment of data points, based on a set of hypothesized probability density functions; The “M-Step” updates the original hypothesis with new data. The cycle repeats until the parameters stabilize.
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