Bit recycling for scaling random number generators

Many Random Number Generators (RNG) are available nowadays; they are divided in two categories, hardware RNG, that provide “true” random numbers, and algorithmic RNG, that generate pseudo random numbers (PRNG). Both types usually generate random numbers $(X_n)$ as independent uniform samples in a range $0,\cdots ,2^{b-1}$, with $b=8,16,32$ or $b=64$. In applications, it is instead sometimes desirable to draw random numbers as independent uniform samples $(Y_n)$ in a range $1,\cdots ,M$, where moreover M may change between drawings. Transforming the sequence $(X_n)$ to $(Y_n)$ is sometimes known as scaling. We discuss different methods for scaling the RNG, both in term of mathematical efficiency and of computational speed.