🚀 go-pugleaf

In article <3638.9209301031@rfsparc.essex.ac.uk> jenum@essex.ac.uk (Mohamed Jenu M Z) writes:
>
>Hi netters, 
>
>Is it ok to generate a 2D Gaussian distribution (mean 0 and variance 1)
>pseudorandom numbers from a 1D pseudorandom numbers (Gaussian, mean 0,
>variance 1) by arranging them into rows and columns? Any advice is
>greatly appreciated. 
>
>Jenu
>
>jenum@ossparc.essex.ac.uk


What about the 2D distribution is supposed to be Gaussian?  If it's
the norm -- the distance from the origin -- , then the answer to your
question is no.  In general, the distribution of a function f(x,y) is
calculated as
         /
  P(z) = | delta(z-f(x,y)) P(x)P(y) dxdy   .
         /
You can see that if x and y are Gaussian, then P(z) will be
proportional to z times a Gaussian, which among other things goes to
zero at the origin.  If you do want z to be normal, you could just
choose it, and then set the azimuth to a number flatly distributed
from 0 to 2pi.  What actually do you want?