## miXed distributions

**A** couple of questions on X validated showed the difficulty students have with mixed measures and their density. Actually, my students always react with incredulity to the likelihood of a censored normal sample or to the derivation of a Bayes factor associated with the null (and atomic) hypothesis μ=0…

I attribute this difficulty to a poor understanding of the notion of density and hence to a deficiency in the training in measure theory, since the density f of the distribution F is always relative to a reference measure dμ, i.e.

f(x) = dF/dμ(x)

(Hence Lebesgue’s moustache on the attached poster!) To handle atoms in the distribution requires introducing a dominating measure dμ with atomic components, i.e., usually a sum of the Lebesgue measure and of the counting measure on the appropriate set. Which is not so absolutely obvious: while the first question had {0,1} as atoms, the second question introduced atoms on {-θ,θ}and required a change of variable to consider a counting measure on {-1,1}. I found this second question actually of genuine interest and a great toy example for class and exams.

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