On Sat, 9 Aug 2014, Ulrich Drepper wrote:
If you are going to specialize for dim 2, I imagine you won't be computing
normal distributions, you will only generate a point uniformy in a square
and reject it if it is not in the ball? (interestingly enough this is used
as a subroutine by the implementation of normal_distribution)
We need to be *on* the circle, not inside.
Yes, you still need the normalization step (divide by the norm). It works
whether you start from a uniform distribution in the disk or from a
Gaussian in the plane, but the first one is easier to generate (generate
points in a square until you get one in the disk). When the dimension
becomes higher, the probability that a point in the cube actually belongs
to the ball decreases, and it becomes cheaper to use a Gaussian.
--
Marc Glisse
