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. More precisely, the model of characterization of probability distribution was described by in such manner. On the probability space we define the space of random variables with values in measurable metric space and the space of random variables with values in measurable metric space . By characterizations of probability distributions we understand general problems of description of some set in the space by extracting the sets and which describe the properties of random variables and their images , obtained by means of a specially chosen mapping . The description of the properties of the random variables and of their images is equivalent to the indication of the set from which must be taken and of the set into which its image must fall. So, the set which interests us appears therefore in the following form: where denotes the complete inverse image of in . This is the general model of characterization of probability distribution. Some examples of characterization theorems: 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. For example, according to George Pólya's characterization theorem, if and are independent identically distributed random variables with finite variance, then the statistics and are identically distributed if and only if and have a normal distribution with zero mean. In this case , is a set of random two-dimensional column-vectors with independent identically distributed components, is a set of random two-dimensional column-vectors with identically distributed components and is a set of two-dimensional column-vectors with independent identically distributed normal components. According to generalized George Pólya's characterization theorem (without condition on finiteness of variance ) if are non-degenerate independent identically distributed random variables, statistics and are identically distributed and , then is normal random variable for any . In this case , is a set of random n-dimensional column-vectors with independent identically distributed components, is a set of random two-dimensional column-vectors with identically distributed components and is a set of n-dimensional column-vectors with independent identically distributed normal components. All probability distributions on the half-line that are memoryless