Generate n-dimensional random numbers in Python
You need to properly decompose your multi-dimensional distribution into a composition of one-dimensional distributions.  For example, if you want a point at a Gaussian-distributed distance from a given center and a uniformly-distributed angle around it, you'll get the polar coordinates for the delta with a Gaussian rho and a uniform theta (between 0 and 2 pi), then, if you want cartesian coordinates, you of course do a coordinate transformation.
I'm trying to generate random numbers from a gaussian distribution. Python has the very useful random.gauss() method, but this is only a one-dimensional random variable. How could I programmatically generate random numbers from this distribution in n-dimensions?
For example, in two dimensions, the return value of this method is essentially distance from the mean, so I would still need (x,y) coordinates to determine an actual data point. I suppose I could generate two more random numbers, but I'm not sure how to set up the constraints.
I appreciate any insights. Thanks!