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Statistical experiments

Small pieces, each one going after a single thing about a model. Rougher than a topic page; rougher on purpose. Two so far.

GAMLSS

Same scatter, three fits

Four datasets, one habit: stack a line, a GAM, and a GAMLSS over the same points and watch what each layer can and can't say. The line is the floor; the GAM bends; the GAMLSS lets the spread bend with it.

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Bayesian

Three problems, three posteriors

A search, a rate, a decision. Each ends with a distribution over the thing you didn't know, and any question you had is now an integral over it. That's the whole framework.

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