Characterization of probability distributions

In mathematics in general, a characterization theorem says that a particular object – a function, a space, etc. – is the only one that possesses properties specified in the theorem. A characterization of a probability distribution accordingly states that it is the only probability distribution that satisfies specified conditions.

For example, according to George Polya's characterization theorem, if $X_{1}$ and $X_{2}$ are independent identically distributed random variables with finite variance, then statistics $S_{1}=X_{1}$ and $S_{2}={\frac {X_{1}+X_{2}}{\sqrt {2}}}$ are identically distributed if and only if $X_{1}$ and $X_{2}$ have the normal distribution with zero mean. The assumption that two linear (or non-linear) statistics are identically distributed (or independent, or have a constancy regression and so on) can be used to characterize various populations (see, for example, ).

See also

• Characterization (mathematics)