Mark Meckes
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 Sep 16 awarded Notable Question Jul 24 revised Do subgaussian variables obey the slightly-stronger-than-Chernoff tail bound? Fixed inequalities. Jul 23 answered Do subgaussian variables obey the slightly-stronger-than-Chernoff tail bound? Jul 20 answered Extending GUE to a measure on operators? Jul 6 awarded Good Question Jun 30 comment Kullback Leibler “variance”: does that divergence have a name? I haven't seen exactly this quantity considered, but something related is studied here (see in particular the discussion after Theorem 1.1): projecteuclid.org/euclid.aop/1312555807 Jun 30 comment Shared maximum eigenvector For an appropriate interpretation of the question, at least. The OP said $v$ is "the" eigenvector associated to the maximum eigenvalue of $A$, but if the eigenspace is not 1-dimensional you may need to replace $v$ with another eigenvector. May 27 comment Higher Moments, what are they good for? Essentially the same question was asked and answered here: stats.stackexchange.com/questions/2893/… May 27 comment Describe the desired features of a “Mathematics Colloquium”? A former department chair of mine used to say that if you asked colloquium speakers to prepare talks appropriate for undergraduates, then there was a chance some of the faculty would understand. May 6 awarded Popular Question May 5 awarded Notable Question Mar 26 comment Is there an introduction to probability theory from a structuralist/categorical perspective? @MartinBrandenburg: Moreover, I would need convincing that probability theory is about the category of probability spaces and measurable maps. That category surely plays a role, but most of what's of interest in the theory seems to take place outside of it. In particular, if you can show me how to view the law of large numbers or the central limit theorem as being about the category of probability spaces and measurable maps, then I'll reconsider. Mar 26 comment Is there an introduction to probability theory from a structuralist/categorical perspective? @MartinBrandenburg: I don't claim that it's distracting to define graphs as pairs of sets. I claim that if you do get distracted by the details of such a definition, then you're not doing graph theory. Nov 14 comment Best possible concentration inequality in high dimensions Ah, or by $\|X_i\|_2 \le 1$ did you mean that the Euclidean norm of $X_i$ is almost surely at most 1? I thought you were referring to the $L^2$ norm of a random variable. In that case, you can e.g. use a vector-valued version of Talagrand's inequality to get the same type of result as the one dimensional case. Nov 14 comment Best possible concentration inequality in high dimensions Yes, in 1 dimension this is Hoeffding's inequality if you have boundedness, but that was never mentioned. Nov 12 comment Best possible concentration inequality in high dimensions You need additional assumptions, even in the one-dimensional case, for Hoeffding's inequality to hold. Oct 22 awarded Yearling Sep 30 awarded Explainer Jul 17 comment Clusters of uniformly distributed random points @ChristianRemling: for the most part, MathSciNet only lists citations from papers indexed in MathSciNet beginning sometime in the 1990s, so it misses almost all citations older than that and many newer ones as well. (It looks like Naus's paper has lots of citations from papers in other fields.) Jul 3 comment Nonexistence of stable random variables I think I really should just read Feller straight through one of these days.