In probability theory and statistics, the moment-generating function of a random variable is an alternative specification of its probability distribution. Thus, it provides the basis of an alternative route to analytical results compared with working directly with probability density functions or cumulative distribution functions. There are particularly simple results for the moment-generating functions of distributions defined by the weighted sums of random variables. Note, however, that not all random variables have moment-generating functions.
In addition to univariate distributions, moment-generating functions can be defined for vector- or matrix-valued random variables, and can even be extended to more general cases.
The moment-generating function does not always exist even for real-valued arguments, unlike the characteristic function. There are relations between the behavior of the moment-generating function of a distribution and properties of the distribution, such as the existence of moments.
wherever this expectation exists.
always exists and is equal to 1.
A key problem with moment-generating functions is that moments and the moment-generating function may not exist, as the integrals need not converge absolutely. By contrast, the characteristic function always exists (because it is the integral of a bounded function on a space of finite measure), and thus may be used instead.
More generally, where T, an n-dimensional random vector, one uses instead of tX:
The reason for defining this function is that it can be used to find all the moments of the distribution. The series expansion of etX is:
where mn is the nth moment.
Differentiating MX(t) i times with respect to t and setting t = 0 we hence obtain the ith moment about the origin, mi, see Calculations of moments below.
Here are some examples of the moment generating function and the characteristic function for comparison. It can be seen that the characteristic function is a Wick rotation of the moment generating function Mx(t) when the latter exists.
|Distribution||Moment-generating function MX(t)||Characteristic function φ(t)|
|Binomial B(n, p)|
|Uniform (continuous) U(a, b)|
|Uniform (discrete) U(a, b)|
|Normal N(μ, σ2)|
|Gamma Γ(k, θ)|
|Multivariate normal N(μ, Σ)|
|Laplace L(μ, b)|
|Negative Binomial NB(r, p)|
|Cauchy Cauchy(μ, θ)||does not exist|
The moment-generating function is the expectation of a function of the random variable, it can be written as:
- In the general case: , using the Riemann–Stieltjes integral, and where F is the cumulative distribution function.
- For a discrete probability mass function,
- For a continuous probability density function,
where mn is the nth moment.
Sum of independent random variables
If X1, X2, ..., Xn is a sequence of independent (and not necessarily identically distributed) random variables, and
where the ai are constants, then the probability density function for Sn is the convolution of the probability density functions of each of the Xi, and the moment-generating function for Sn is given by
Vector-valued random variables
where t is a vector and is the dot product.
Moment generating functions are positive and log-convex, with M(0) = 1.
An important property of the moment-generating function is that if two distributions have the same moment-generating function, then they are identical at almost all points. That is, if for all values of t,
for all values of x (or equivalently X and Y have the same distribution). This statement is not equivalent to the statement "if two distributions have the same moments, then they are identical at all points." This is because in some cases, the moments exist and yet the moment-generating function does not, due to the fact that the limit
may not exist. The lognormal distribution is an example of when this occurs.
Calculations of moments
Here n should be a nonnegative integer.
Hoeffding's lemma provides a bound on the moment-generating function in the case of a zero-mean, bounded random variable.
Relation to other functions
Related to the moment-generating function are a number of other transforms that are common in probability theory:
- characteristic function
- The characteristic function is related to the moment-generating function via the characteristic function is the moment-generating function of iX or the moment generating function of X evaluated on the imaginary axis. This function can also be viewed as the Fourier transform of the probability density function, which can therefore be deduced from it by inverse Fourier transform.
- cumulant-generating function
- The cumulant-generating function is defined as the logarithm of the moment-generating function; some instead define the cumulant-generating function as the logarithm of the characteristic function, while others call this latter the second cumulant-generating function.
- probability-generating function
- The probability-generating function is defined as This immediately implies that
||This article includes a list of references, but its sources remain unclear because it has insufficient inline citations. (February 2010)|
- Grimmett, Geoffrey; Welsh, Dominic. Probability - An Introduction (1st ed.). pp. 101 ff. ISBN 978-0-19-853264-4.