Factorial moment generating function

In probability theory and statistics, the factorial moment generating function (FMGF) of the probability distribution of a real-valued random variable X is defined as

[math]\displaystyle{ M_X(t)=\operatorname{E}\bigl[t^{X}\bigr] }[/math]

for all complex numbers t for which this expected value exists. This is the case at least for all t on the unit circle [math]\displaystyle{ |t|=1 }[/math], see characteristic function. If X is a discrete random variable taking values only in the set {0,1, ...} of non-negative integers, then [math]\displaystyle{ M_X }[/math] is also called probability-generating function (PGF) of X and [math]\displaystyle{ M_X(t) }[/math] is well-defined at least for all t on the closed unit disk [math]\displaystyle{ |t|\le1 }[/math].

The factorial moment generating function generates the factorial moments of the probability distribution. Provided [math]\displaystyle{ M_X }[/math] exists in a neighbourhood of t = 1, the nth factorial moment is given by

[math]\displaystyle{ \operatorname{E}[(X)_n]=M_X^{(n)}(1)=\left.\frac{\mathrm{d}^n}{\mathrm{d}t^n}\right|_{t=1} M_X(t), }[/math]

where the Pochhammer symbol (x)_n is the falling factorial

[math]\displaystyle{ (x)_n = x(x-1)(x-2)\cdots(x-n+1).\, }[/math]

(Many mathematicians, especially in the field of special functions, use the same notation to represent the rising factorial.)

Examples

Poisson distribution

Suppose X has a Poisson distribution with expected value λ, then its factorial moment generating function is

[math]\displaystyle{ M_X(t) =\sum_{k=0}^\infty t^k\underbrace{\operatorname{P}(X=k)}_{=\,\lambda^ke^{-\lambda}/k!} =e^{-\lambda}\sum_{k=0}^\infty \frac{(t\lambda)^k}{k!} = e^{\lambda(t-1)},\qquad t\in\mathbb{C}, }[/math]

(use the definition of the exponential function) and thus we have

[math]\displaystyle{ \operatorname{E}[(X)_n]=\lambda^n. }[/math]

See also

• Moment (mathematics)
• Moment-generating function
• Cumulant-generating function