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  "body": "\n## Summary\n\nThe expectation–maximization (EM) algorithm is an iterative method to compute maximum likelihood estimates when the data are incomplete or the model has latent variables.\nEach iteration has two steps.\nThe expectation step averages the complete-data log-likelihood over the missing part, given the current parameter; the maximization step maximizes this average.\nEach iteration does not decrease the likelihood of the observed data.\nIt is the standard method for fitting mixture models and hidden Markov models; Dempster, Laird and Rubin gave its general formulation in 1977.\n\n## Explanations\n\n### L1 human\n\nSuppose heights come from a mix of two groups, but nobody recorded who belongs to which group.\nIf you knew the groups, estimating each group's average would be easy; if you knew the averages, guessing the groups would be easy.\nEM goes back and forth: it guesses soft group memberships from the current averages, then recomputes the averages from these memberships, and repeats.\nEach round makes the fit to the data at least as good as before.\n",
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