Entropy of a probability distribution

The entropy of a probability distribution is a number that describes the degree of uncertainty or disorder the distribution represents.

Examples

Assume we have a set of two mutually exclusive propositions (or equivalently, a random experiment with two possible outcomes). Assume all two possibilities are equally likely.

Then our advance uncertainty about the eventual outcome is rather small - we know in advance it will be one of exactly two known alternatives.

Assume now we have a set of a million alternatives - all of them equally likely - rather than two.

It seems clear that our uncertainty now about the eventual outcome will be much bigger.

Formal definitions

1. Given a discrete probability distribution function f, the entropy H of the distribution (measured in bits) is given by $H=-\sum _{\forall i:f(x_{i})\neq 0}^{}f(x_{i})log_{2}f(x_{i})$
2. Given a continuous probability distribution function f, the entropy H of the distribution (again measure in bits) is given by $H=-\int _{\ x:f(x)\neq 0}f(x)log_{2}f(x)dx$

Note that some authors prefer to use other units than bit to measure entropy, the formulas are then slightly different. Also, the symbol S is sometimes used, rather than H.

See also

• Entropy in thermodynamics and information theory
• Discrete probability distribution
• Continuous probability distribution
• Probability
• Probability theory