# Characteristic function (probability theory)

In probability theory, the **characteristic function** of any random variable completely defines its probability distribution. On the real line it is given by the following formula, where *X* is any random variable with the distribution in question:

where *t* is a real number, *i* is the imaginary unit, and E denotes the expected value.

If *F*_{X} is the cumulative distribution function, then the characteristic function is given by the Riemann-Stieltjes integral

In cases in which there is a probability density function, *f*_{X}, this becomes

If *X* is a vector-valued random variable, one takes the argument *t* to be a vector and *tX* to be a dot product.

Every probability distribution on **R** or on **R**^{n} has a characteristic function, because one is integrating a bounded function over a space whose measure is finite, and for every characteristic function there is exactly one probability distribution.

The characteristic function of a symmetric PDF (that is, one with ) is real, because the imaginary components obtained from cancel those from .

## Contents

## Lévy continuity theorem

The core of the **Lévy continuity theorem** states that a sequence of random variables where each has a characteristic function
will converge in distribution towards a random variable ,

if

and continuous in and is the characteristic function of .

The Lévy continuity theorem can be used to prove the weak law of large numbers, see the proof using convergence of characteristic functions.

## The inversion theorem

More than that, there is a bijection between cumulative probability distribution functions and characteristic functions. In other words, two distinct probability distributions never share the same characteristic function.

Given a characteristic function φ, it is possible to reconstruct the corresponding cumulative probability distribution function *F*:

In general this is an improper integral; the function being integrated may be only conditionally integrable rather than Lebesgue integrable, i.e. the integral of its absolute value may be infinite.

## Bochner-Khinchin theorem

An arbitrary function is a characteristic function corresponding to some probability law if and only if the following three conditions are satisfied:

(1) is continuous

(2)

(3) is a positive definite function

## Uses of characteristic functions

Because of the continuity theorem, characteristic functions are used in the most frequently seen proof of the central limit theorem. The main trick involved in making calculations with a characteristic function is recognizing the function as the characteristic function of a particular distribution.

### Basic properties

Characteristic functions are particularly useful for dealing with functions of independent random variables. For example, if *X*_{1}, *X*_{2}, ..., *X*_{n} is a sequence of independent (and not necessarily identically distributed) random variables, and

where the *a*_{i} are constants, then the characteristic function for *S*_{n} is given by

In particular, . To see this, write out the definition of characteristic function:

- .

Observe that the independence of and is required to establish the equality of the third and fourth expressions.

Another special case of interest is when and then is the sample mean. In this case, writing for the mean,

### Moments

Characteristic functions can also be used to find moments of a random variable. Provided that the *n*^{th} moment exists, characteristic function can be differentiated *n* times and

For example, suppose has a standard Cauchy distribution. Then . See how this is not differentiable at , showing that the Cauchy distribution has no expectation. Also see that the characteristic function of the sample mean of independent observations has characteristic function , using the result from the previous section. This is the characteristic function of the standard Cauchy distribution: thus, the sample mean has the same distribution as the population itself.

### An example

The Gamma distribution with scale parameter *θ* and a shape parameter *k* has the characteristic function

Now suppose that we have

with *X* and *Y* independent from each other, and we wish to know what the distribution of *X* + *Y* is. The characteristic functions are

which by independence and the basic properties of characteristic function leads to

This is the characteristic function of the gamma distribution scale parameter *θ* and shape parameter *k*_{1} + *k*_{2}, and we therefore conclude

The result can be expanded to *n* independent gamma distributed random variables with the same scale parameter and we get

## Multivariate characteristic functions

If is a multivariate PDF, then its characteristic function is defined as

Here, the dot signifies vector dot product ( is in the dual space of ).

## Related concepts

Related concepts include the moment-generating function and the probability-generating function. The characteristic function exists for all probability distributions. However this is not the case for moment generating function.

The characteristic function is closely related to the Fourier transform: the characteristic function of a probability density function is the complex conjugate of the continuous Fourier transform of (according to the usual convention; see [1]).

where denotes the continuous Fourier transform of the probability density function . Likewise, may be recovered from through the inverse Fourier transform:

Indeed, even when the random variable does not have a density, the characteristic function may be seen as the Fourier transform of the measure corresponding to the random variable.

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