Python, Statistics, Distribution, Gaussian Distribution, Normal Distribution.
is true for negative values. η serves as the scale parameter. import matplotlib.pyplot as plt x = np.arange(1,100.)/50. def weib(x,n,a): return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a) count, bins, ignored = plt.hist(np.random.weibull(5.,1000)) x = np.arange(1,100.)/50. scale =