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You may choose from these distributions: exponential, uniform, triangular, Pareto, Gaussian, Rayleigh, binomial, negative binomial, geometric, and Poisson.

The method of inverse transforms is most often used to simulate a realization of a random variable associated with a particular distribution. Inverse transform sampling works as follows.

Consider, for example, a continuous random variable with cumulative distribution function . Let be a uniform random variable over the unit interval and pass through the inverse of the cumulative distribution function, that is, compute . We call the sample.

It can be seen that for a sufficiently large set of samples, the associated normalized histogram generates a close approximation to the probability density function of the random variable associated with the cumulative distribution function . A result of Kolmogorov asserts that if is continuous, then the rate of convergence in the sup-norm topology is .

For more information about generating samples from random variables, see, for example, S. M. Ross, Simulation, 3rd ed., London: Elsevier Academic Press, 2002.