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Random scalars definitely don't seem like somethign that needs radical technical solutions, no. I wonder if the tricky part is in choosing random vectors? Just
by jameshart 5y ago
Random scalars definitely don't seem like somethign that needs radical technical solutions, no. I wonder if the tricky part is in choosing random vectors?
Just doing simple things like choosing a uniformly distributed random point in a disk or on the surface of a sphere are awkward enough to do efficiently; choosing a bunch of random points drawn from the London temperature/pressure/rainfall distribution for December is much worse. I wonder if that might be part of the complexity that is being elided to bring this article to a popular-science-reader level?
- acidburnNSA 5y agoMaybe? Sampling on a sphere is computationally trivial in spherical coordinates and done in every undergraduate Monte Carlo radiation transport class as a homework. Knowing temperature PDFs is similarly easy, and so is sampling them. I will have to go read some literature on this cointoss business I think.
- klipt 5y ago> I wonder if the tricky part is in choosing random vectors? Sampling vectors from multidimensional random distributions is a well studied problem. For example the open source project https://mc-stan.org https://mc-stan.org implemented several (at the time) state of the art methods like Hamiltonian Monte Carlo with the help of Automatic Differentiation.