# Shannon entropy

## English

### Etymology

Named after Claude Shannon, the "father of information theory".

### Noun

Shannon entropy (countable and uncountable, plural Shannon entropies)

1. information entropy
Shannon entropy H is given by the formula $H=-\sum _{i}p_{i}\log _{b}p_{i}$ where pi is the probability of character number i appearing in the stream of characters of the message.
Consider a simple digital circuit which has a two-bit input (X, Y) and a two-bit output (X and Y, X or Y). Assuming that the two input bits X and Y have mutually independent chances of 50% of being HIGH, then the input combinations (0,0), (0,1), (1,0), and (1,1) each have a 1/4 chance of occurring, so the circuit's Shannon entropy on the input side is $H(X,Y)=4{\Big (}-{1 \over 4}\log _{2}{1 \over 4}{\Big )}=2$ . Then the possible output combinations are (0,0), (0,1) and (1,1) with respective chances of 1/4, 1/2, and 1/4 of occurring, so the circuit's Shannon entropy on the output side is $H(X{\text{ and }}Y,X{\text{ or }}Y)=2{\Big (}-{1 \over 4}\log _{2}{1 \over 4}{\Big )}-{1 \over 2}\log _{2}{1 \over 2}=1+{1 \over 2}=1{1 \over 2}$ , so the circuit reduces (or "orders") the information going through it by half a bit of Shannon entropy due to its logical irreversibility.