beta-distribution

I need to generate random numbers that have a beta distribution in some speed critical code. Currently I'm using the BetaRandomVariable() class from the numerics4j library - but currently represents about 95% of my code's CPU usage! Can anyone recommend a faster way to generate these random numbers? You can look at the NumPy random number generators . They're in C, but the code is relatively readable and permissively licensed, so they can easily be ported to Java. You don't need to port the basic Mersenne Twister generator as Java has one in its standard library, only the code that takes

I am trying to get a correct way of fitting a beta distribution. It's not a real world problem i am just testing the effects of a few different methods, and in doing this something is puzzling me. Here is the python code I am working on, in which I tested 3 different approaches: 1>: fit using moments (sample mean and variance). 2>: fit by minimizing the negative log-likelihood (by using scipy.optimize.fmin()). 3>: simply call scipy.stats.beta.fit() from scipy.optimize import fmin from scipy.stats import beta from scipy.special import gamma as gammaf import matplotlib.pyplot as plt import numpy