RandomState.power(a, size=None) Draws samples in [0, 1] from a power distribution with positive exponent a - 1

numpy.random.random(size=None) Return random floats in the half-open interval [0.0, 1.0). Results are from the

RandomState.permutation(x) Randomly permute a sequence, or return a permuted range. If x

numpy.random.noncentral_chisquare(df, nonc, size=None) Draw samples from a noncentral chi-square distribution

numpy.random.uniform(low=0.0, high=1.0, size=None) Draw samples from a uniform distribution. Samples are uniformly

numpy.random.randn(d0, d1, ..., dn) Return a sample (or samples) from the ?standard normal? distribution. If positive

numpy.random.multinomial(n, pvals, size=None) Draw samples from a multinomial distribution. The multinomial

numpy.random.normal(loc=0.0, scale=1.0, size=None) Draw random samples from a normal (Gaussian) distribution. The

RandomState.noncentral_f(dfnum, dfden, nonc, size=None) Draw samples from the noncentral F distribution

numpy.random.multivariate_normal(mean, cov[, size]) Draw random samples from a multivariate normal distribution