Randomizing Functions: Simulation of a Discrete Probability Distribution Using a Source of Unknown Distribution

In this paper, we characterize functions that simulate independent unbiased coin flips from independent coin flips of unknown bias. We call such functions randomizing. Our characterization of randomizing functions enables us to identify the functions that generate the largest average number of fair coin flips from a fixed number of biased coin flips. We show that these optimal functions are efficiently computable. Then we generalize the characterization, and we present a method to simulate an arbitrary rational probability distribution optimally (in terms of the average number of output digits) and efficiently (in terms of computational complexity) from outputs of many-faced dice of unknown distribution. We also study randomizing functions on exhaustive prefix-free sets