bio  website  case.edu/artsci/math/mwmeckes 

location  Cleveland, Ohio  
age  38  
visits  member for  5 years, 8 months 
seen  Jul 2 at 9:38  
stats  profile views  4,270 
Assoc. Prof. at CWRU. My main research interests are in probability, geometry, and functional analysis.
1h

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 
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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 1dimensional 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 
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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 
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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 
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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 
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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 vectorvalued version of Talagrand's inequality to get the same type of result as the one dimensional case. 
Nov 14 
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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 
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Best possible concentration inequality in high dimensions
You need additional assumptions, even in the onedimensional case, for Hoeffding's inequality to hold. 
Oct 22 
awarded  Yearling 
Sep 30 
awarded  Explainer 
Jul 17 
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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 
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Nonexistence of stable random variables
I think I really should just read Feller straight through one of these days. 
Jul 2 
awarded  Curious 
Jul 2 
accepted  Nonexistence of stable random variables 
Jul 2 
comment 
Functions that Calculate their $L_p$ Norm
How about $a=0$, $b=1$, $f(x) = 1$? 
Jul 2 
comment 
Nonexistence of stable random variables
Here's a purely probabilistic proof that if nonzero $\alpha$stable variables with finite second moment exist then $\alpha = 2$. Suppose that $X$ is $\alpha$stable with finite second moment, and assume without loss of generality that $X$ is symmetric, so that $\mathbb{E} X = 0$. Then $$ \mathbb{E} X^2 = \mathbb{E} \left(n^{1/\alpha} \sum_{i=1}^n X_i \right)^2 = n^{2/\alpha} \left(\sum_{i=1}^n \mathbb{E} X_i^2 + \sum_{i\neq j} \mathbb{E} X_i X_j \right) = n^{12/\alpha} \mathbb{E} X^2, $$ and so $\alpha = 2$. 