Timeline for Why don't Bayesian methods require multiple testing corrections?

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Mar 24, 2016 at 18:40	history	edited	amoeba	CC BY-SA 3.0	typos
Mar 24, 2016 at 15:10	comment	added	probabilityislogic		@StasK - l1 would work better, but as it is log-concave would struggle with sparse non-zeros. The ones I mentioned are all log-convex. A close variant to l1 is generalised double pareto - get by taking a mixture of laplace scale parameter (similar to adaptive lasso in ML speak)
Mar 24, 2016 at 15:02	comment	added	StasK		So one way to squish coefficients to be zeroes unless there's really something going on is lasso; in the frequentist version of it, you apply an $l_1$ norm on the sum of the coefficients, and in the Bayesian version of it, you use sharply peaked priors (Laplace $\exp(-\|x\|)$). So in this case, knowing that you want to have mostly zeroes and a handful of nonzeroes in your output, you modify the prior to correspond to that statement that zero is a much more likely value than any other (vs. the normal prior's statement that values near zero are about as likely as the zero itself).
Mar 24, 2016 at 14:57	history	answered	probabilityislogic	CC BY-SA 3.0