Binary entropy function

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In information theory, the binary entropy function, denoted $H(p) \,$ or $H_{\mathrm b}(p) \,$ , is defined as the entropy of a Bernoulli trial with probability of success p. Mathematically, the Bernoulli trial is modelled as a random variable X that can take on only two values: 0 and 1. The event $X = 1$ is considered a success and the event $X = 0$ is considered a failure. (These two events are mutually exclusive and exhaustive.)

If $Pr(X = 1) = p,$ then $Pr(X = 0) = 1 - p$ and the entropy of X is given by

$H(X) = H_{\mathrm b}(p) = -p \log p - (1 - p) \log (1 - p). \,$

The logarithms in this formula are usually taken (as shown in the graph) to the base 2. See binary logarithm.

When $p=\frac 1 2 ,$ the binary entropy function attains its maximum value. This is the case of the unbiased bit, the most common unit of information entropy.

$H (p)$ is distinguished from the entropy function by its taking a single scalar constant parameter. For tutorial purposes, in which the reader may not distinguish the appropriate function by its argument, $H 2 (p)$ is often used; however, this could confuse this function with the analogous function related to Rényi entropy, so $H b (p)$ (with "b" not in italics) should be used to dispel ambiguity.