NumPy : Beginner's Guide - Third Edition

Continuous distributions

We usually model continuous distributions with probability density functions (PDF). The probability that a value is in a certain interval is determined by integration of the PDF (see https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/probability-density-functions). The NumPy random module has functions that represent continuous distributions—beta(), chisquare(), exponential(), f(), gamma(), gumbel(), laplace(), lognormal(), logistic(), multivariate_normal(), noncentral_chisquare(), noncentral_f(), normal(), and others.