Tilo Wiklund
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 1d comment Software for organising mathematics This is not a bad suggestion, though I feel it still falls mostly within the org-mode/outliner to jupyter/sage notebook spectrum. I think my original hope was for something more "free-form". Apr 25 awarded Notable Question Feb 26 comment Notation for the collection of all probability distributions on a (measurable) space Liese and Miescke (springer.com/gp/book/9780387731933, see for example page 632 in the freely available back matter) use $\mathcal{P}(\Sigma)$, which I've seen used a couple of times. That being said, it has the obvious issue that you need a different notation for powersets (they use $\mathfrak{P}$). Jun 6 awarded Popular Question Jun 6 comment Posets as Categories and Direct Systems of Objects? One way to think of those $\rho_b^a$ is as witnesses, that is to say, roughly speaking, they're proofs that $a$ is less than $b$. Of course, this does not strictly speaking work here, since there might exist multiple proofs of this fact (while you have at most one morphism between any pair of objects), meaning we have to think of it as having taken some sort of quotient. In the end this just means that you have a single "token" for each pair $a \preceq b$, with the order axioms then corresponding to closure properties of the category. May 27 comment Hoeffdingâ€™s inequality extension You will need some sort of control over their dependence. May 27 comment How to combine 2 different variables (time&cost) into a fitness function? Balancing different losses will pretty much always reduce to non-mathematical questions. What you might want to do is change two of your variables into constraints, and optimise for the final one. If you do this for a number of different values on the constraints you get a picture of how they affect each other, and base your final decision on this behaviour. Alternatively you might want to develop a cost model that reduces things to a single value (e.g. probability of people choosing to go by taxi). Apr 15 awarded Nice Question Jan 21 answered What's your favorite proof accessible to a general audience? Jul 19 awarded Yearling Jul 2 awarded Curious Apr 4 awarded Custodian Apr 4 reviewed No Action Needed Linear Inequalities - Allocation Problem Apr 4 comment Normal Distribution - Statistics @ScottGoddard Join ##statistics (or ##math, I'm on both) on irc.freenode.net, stackexchange isn't meant for lengthy discussions. Apr 4 comment How do we square a random variable? I think you might have gotten integration limits or similar wrong (nothing you shouldn't be able to fix by just rereading what you did, could also just be me who's handicappted at computation :)), otherwise it looks reasonable. Apr 4 comment Normal Distribution - Statistics Note that one has to impose some requirements on the distribution you're taking averages of the observations of (which basically make sure that taking averages makes any sense to begin with, check wikipedia for details). Apr 4 comment Normal Distribution - Statistics @ScottGoddard Getting into details about CLT gets kind of technical, esp. once you get to its generalisations. For the common case replace "averaging operations" by "sample mean of independent variables" (say, the average of multiple independent measurements of some fixed quantity to reduce instrument noise). If you do this multiple times for different sets of independent observations, you get multiple such "averages", the CLT now states that as the number of observations in each sample gets large, the collections of averages look more and more like being taken from a normal distribution. Apr 4 comment Normal Distribution - Statistics Is this a jeopardy question, where we're to figure out the question given the answers :)? Also, the central limit theorem does not imply that any sample can be approximated by a normal distribution (which I guess is supposed to mean that the empirical distribution of a sample is approximately normal), it merely states that certain kinds of averaging operations yield (asymptotically) normally distributed things, when applied to sufficiently well behaved distributions. Apr 4 comment How do we square a random variable? Have you tried taking the derivative of \$F(t)=P(Y