In probability theory, the probability generating function of a discrete random variable is a power series representation (the generating function) of the probability mass function of the random variable. Probability generating functions are often employed for their succinct description of the sequence of probabilities Pr(X = i) in the probability mass function for a random variable X, and to make available the well-developed theory of power series with non-negative coefficients.
Definition
Univariate case If X is a discrete random variable taking values x in the non-negative integers {0,1, ...}, then the probability generating function of X is defined as
G ( z ) = E ( z X ) = ∑ x = 0 ∞ p ( x ) z x , {\displaystyle G(z)=\operatorname {E} (z^{X})=\sum _{x=0}^{\infty }p(x)z^{x},}
where p {\displaystyle p} is the probability mass function of X {\displaystyle X} . Note that the subscripted notations G X {\displaystyle G_{X}} and p X {\displaystyle p_{X}} are often used to emphasize that these pertain to a particular random variable X {\displaystyle X} , and to its distribution. The power series converges absolutely at least for all complex numbers z {\displaystyle z} with | z | < 1 {\displaystyle |z|<1} ; the radius of convergence being often larger.
Multivariate case If X = (X1,...,Xd) is a discrete random variable taking values (x1, ..., xd) in the d-dimensional non-negative integer lattice {0,1, ...}d, then the probability generating function of X is defined as
G ( z ) = G ( z 1 , … , z d ) = E ( z 1 X 1 ⋯ z d X d ) = ∑ x 1 , … , x d = 0 ∞ p ( x 1 , … , x d ) z 1 x 1 ⋯ z d x d , {\displaystyle G(z)=G(z_{1},\ldots ,z_{d})=\operatorname {E} {\bigl (}z_{1}^{X_{1}}\cdots z_{d}^{X_{d}}{\bigr )}=\sum _{x_{1},\ldots ,x_{d}=0}^{\infty }p(x_{1},\ldots ,x_{d})z_{1}^{x_{1}}\cdots z_{d}^{x_{d}},}
where p is the probability mass function of X. The power series converges absolutely at least for all complex vectors z = ( z 1 , . . . z d ) ∈ C d {\displaystyle z=(z_{1},...z_{d})\in \mathbb {C} ^{d}} with max { | z 1 | , . . . , | z d | } ≤ 1. {\displaystyle {\text{max}}\{|z_{1}|,...,|z_{d}|\}\leq 1.}
Properties
Power series Probability generating functions obey all the rules of power series with non-negative coefficients. In particular, G ( 1 − ) = 1 {\displaystyle G(1^{-})=1} , where G ( 1 − ) = lim x → 1 , x < 1 G ( x ) {\displaystyle G(1^{-})=\lim _{x\to 1,x<1}G(x)} , x approaching 1 from below, since the probabilities must sum to one. So the radius of convergence of any probability generating function must be at least 1, by Abel's theorem for power series with non-negative coefficients.
Probabilities and expectations The following properties allow the derivation of various basic quantities related to X {\displaystyle X} :
… excerpt ends here. Continue reading the full article.
