Category: Information theory

Entropy extractor used in ╬╝RNG

Yesterday I mentioned μRNG, a true random number generator (TRNG) that takes physical sources of randomness as input. These sources are independent but non-uniform. This post will present the entropy extractor μRNG uses to take non-uniform bits as input and produce uniform bits as output. We will present Python code for playing with the entropy extractor. (μRNG […]

Solving for probability given entropy

If a coin comes up heads with probability p and tails with probability 1-p, the entropy in the coin flip is S = –p log2 p – (1-p) log2 (1-p). It’s common to start with p and compute entropy, but recently I had to go the other way around: given entropy, solve for p. It’s easy to come up […]