  For any discrete probability distributions with bounded entropy, we can
generate exactly a random variate using only a finite expected number of
perfect coin flips. A perfect coin flip is the outcome of an unbiased Bernoulli
random variable. Coin flips are unbiased, independently and identically
distributed in all our work. We survey well-known algorithms for the discrete
case such as the one from Knuth and Yao as well as the one from Han and Hoshi.
We also discuss briefly about a practical implementation for the algorithm
proposed by Knuth and Yao. For the continuous case, only approximations can be
hoped for. The freedom to choose the accuracy for the approximations matters,
and, for that, we propose to measure accuracy in terms of the Wasserstein
$L_\infty$-metric. We derive a universal lower bound for the expected number of
perfect coin flips required to reach a desired accuracy. We also provide
several algorithms for absolutely continuous distributions that come within our
universal lower bound.
