Gaby Contardo (Flatiron) and I discussed our project on voids and gaps in data. We have to write! That means that we have to put into words things we have been doing in code. One of our realizations is that there is a very useful statistic, which is the largest eigenvalue of the second derivative tensor of the density (estimate), projected onto the subspace orthogonal to the gradient (the first derivative). It's obvious that this is a good idea. But it isn't obvious why or how to explain to others why this is a good idea. Contardo assigned me this as homework this week.
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