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The EM Algorithm and Hidden Variables

Mixtures of Gaussians as the standard case of learning without labels, expected counts as the substitute for counts, the guarantee that the likelihood never falls, and a measured case where soft assignment recovers a structure hard assignment cannot.

IntermediateModule 330 min · 120 XP
Twenty unlabelled points on a line acquiring fractional colour as two Gaussians are fitted over them, the log likelihood climbing beside the axis, and one disputed point at 1.7 changing hands when width is allowed to matter.

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