Inverse transform sampling

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Inverse transform sampling is a method of sampling a number at random from any probability distribution given its cumulative distribution function (cdf). This method is generally applicable, but may be too computationally expensive in practice for some probability distributions. See Box-Muller transform for an example of an algorithm which is less general but more computationally efficient.

[edit] The method

The problem that the inverse transform sampling method solves is as follows:

• Let X be a random variable whose distribution can be described by the cdf F.
• We want to generate values of X which are distributed according to this distribution.

Many programming languages have the ability to generate pseudo-random numbers which are effectively distributed according to the standard uniform distribution. If a random variable has that distribution, then the probability of its falling within any subinterval (a, b) of the interval from 0 to 1 is just the length b − a of that subinterval.

The inverse transform sampling method works as follows:

1. Generate a random number from the standard uniform distribution; call this u.
2. Compute the value x such that F(x) = u; call this x_chosen.
3. Take x_chosen to be the random number drawn from the distribution described by F.

Expressed differently, given a continuous uniform variable U in [0, 1] and an invertible distribution function F, the random variable X = F ^{− 1}(U) has distribution F (or, X is distributed F).

[edit] Proof of correctness

Let F be a continuous cumulative distribution function, and let F ^{− 1} be its inverse function:^[1]

Claim: If U is a uniform random variable on (0;1) then F ^{− 1}(U) follows the distribution F.

Proof:

   (by the definition of F ^{− 1})

   (applying F, which is monotonic, to both sides)

   (because , since U is uniform on the unit interval)

[edit] References

1. ^ Luc Devroye. Non-Uniform Random Variate Generation. New York: Springer-Verlag, 1986. (online) See chapter 2, section 2, p. 28.