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Priors, Smoothing, and Naive Bayes

What maximum likelihood does to an event it has never seen, why a Beta prior is the natural repair, and the classifier that takes the whole apparatus, assumes away every dependence, and works anyway.

IntermediateModule 225 min · 100 XP
A Beta density starting symmetric and sharpening as observations arrive, with the maximum-likelihood spike, the MAP point and the posterior mean tracked separately as they converge, then a naive Bayes product collapsing to zero on a single missing count.

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