example algorithm for generating random value in dataset with normal distribution?

Question

I'm trying to generate some random numbers with simple non-uniform probability to mimic lifelike data for testing purposes. I'm looking for a function that accepts mu and sigma as parameters and returns x where the probably of x being within certain ranges follows a standard bell curve, or thereabouts. It needn't be super precise or even efficient. The resulting dataset needn't match the exact mu and sigma that I set. I'm just looking for a relatively simple non-uniform random number generator. Limiting the set of possible return values to ints would be fine. I've seen many suggestions out there, but none that seem to fit this simple case.

Answer 1

First, get two independent, uniform random numbers from the interval (0, 1], call them U and V.

Then you can get two independent, unit-normal distributed random numbers from the formulae

X = sqrt(-2 * log(U)) * cos(2 * pi * V);
Y = sqrt(-2 * log(U)) * sin(2 * pi * V);

This gives you random numbers for mu = 0, sigma = 1; to set sigma = s, multiply your random numbers by s; to set mu = m, add m to your random numbers.