In information theory, the binary entropy function, denoted H ( p ) {\displaystyle \operatorname {H} (p)} or H b ( p ) {\displaystyle \operatorname {H} _{\text{b}}(p)} , is defined as the entropy of a Bernoulli process (i.i.d. binary variable) X {\displaystyle X} with probability p {\displaystyle p} of one of two values, and is given by the formula: H ( X ) = − p log p − ( 1 − p ) log ( 1 − p ) .
In information theory, the binary entropy function, denoted H ( p ) {\displaystyle \operatorname {H} (p)} or H b ( p ) {\displaystyle \operatorname {H} _{\text{b}}(p)} , is defined as the entropy of a Bernoulli process (i.i.d. binary variable) X {\displaystyle X} with probability p {\displaystyle p} of one of two values, and is given by the formula: H ( X ) = − p log p − ( 1 − p ) log ( 1 − p ) .
Source: Wikipedia "Binary entropy function" · CC BY-SA 4.0
Share this article: X · Bluesky