The probability-generating function of a discrete random variable is a power series representation of the probability mass function of the random variable.
If is a discrete random variable taking values in the non-negative integers
is a discrete random variable taking values in the non-negative integers then the probability-generating function of
then the probability-generating function of is defined as
is defined as
where is the probability mass function of
is the probability mass function of
If is a discrete random variable taking values in the d-dimensional non-negative integer lattice
 is a discrete random variable taking values in the d-dimensional non-negative integer lattice then the probability-generating function of
then the probability-generating function of is defined as
is defined as
where is the probability mass function of X. The power series converges absolutely for all complex vectors
is the probability mass function of X. The power series converges absolutely for all complex vectors with
with
Probability generating functions obey all the rules of power series with non-negative coefficients.
The following properties allow the derivation of various basic quantities related to
1. The probability mass function of is recovered by taking derivatives of
is recovered by taking derivatives of
2. If two probability distributions has the same probability generating function then they are the same distribution.
The expectation of is given by
is given by where
where indicates
indicates from below.
from below.
More generally, the kth factorial moment, of X is given by
of X is given by So the variance of X is given by
So the variance of X is given by
3. where
where is a random variable,
is a random variable, is the probability generating function and
is the probability generating function and is the moment-generating function.
is the moment-generating function.
Probability-generating functions are useful when several independent random variables are involved. For example:
-  If  is a sequence of independent random variables, and is a sequence of independent random variables, and 
- where the are constants, then the probability-generating function is given by are constants, then the probability-generating function is given by  
-  For example, if S-n =SUM^n-{i=1} X-i then  
-  It follows that the probability-generating function of the difference of two independent random variables is is  
Example:
-  The probability-generating function of a constant random variable, i.e. one with  is is 
-  The probability-generating function of a binomial random variable, the number of successes in  trials, with probability trials, with probability of success in each trial, is of success in each trial, is 
-  The probability-generating function of a negative binomial random variable, the number of failures occurring before the  th success with probability of success in each trial th success with probability of success in each trial is is
  
-  Note that this is the th power of the probability generating function of a geometric random variable. th power of the probability generating function of a geometric random variable.
-  The probability-generating function of a Poisson random variable with rate parameter  is is
 
  
  
 