Probability generating function

Properties

Power series

Probability generating functions obey all the rules of power series with non-negative coefficients. In particular, ${\displaystyle G(1^{-})=1}$, where ${\displaystyle G(1^{-})=\underset{x\to 1,x<1}{lim}G(x)}$, 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 ${\displaystyle X}$:

1. The probability mass function of ${\displaystyle X}$ is recovered by taking derivatives of ${\displaystyle G}$, $${\displaystyle p(k)=\mathrm{Pr}(X=k)=\frac{G^{(k)}(0)}{k!}.}$$
2. It follows from Property 1 that if random variables ${\displaystyle X}$ and ${\displaystyle Y}$ have probability generating functions that are equal, ${\displaystyle G_X=G_Y}$, then ${\displaystyle p_X=p_Y}$. That is, if ${\displaystyle X}$ and ${\displaystyle Y}$ have identical probability generating functions, then they have identical distributions.
3. The normalization of the probability mass function can be expressed in terms of the generating function by $${\displaystyle \mathrm{E}[1]=G(1^{-})=\sum _{i=0}^{\mathrm{\infty }}p(i)=1.}$$ The expectation of ${\displaystyle X}$ is given by $${\displaystyle \mathrm{E}[X]=G^{\prime }(1^{-}).}$$ More generally, the ${\displaystyle k^{th}}$factorial moment, ${\displaystyle \mathrm{E}[X(X-1)\cdots (X-k+1)]}$ of ${\displaystyle X}$ is given by $${\displaystyle \mathrm{E}\left[\frac{X!}{(X-k)!}\right]=G^{(k)}(1^{-}),k\ge 0.}$$ So the variance of ${\displaystyle X}$ is given by $${\displaystyle \mathrm{Var}(X)=G^{″}(1^{-})+G^{\prime }(1^{-})-{\left[G^{\prime }(1^{-})\right]}^2.}$$ Finally, the k-th raw moment of X is given by $${\displaystyle \mathrm{E}[X^k]={\left(z\frac{\mathrm{\partial }}{\mathrm{\partial }z}\right)}^{k}G(z){|}_{z=1^{-}}}$$
4. ${\displaystyle G_X(e^t)=M_X(t)}$ where X is a random variable, ${\displaystyle G_X(t)}$ is the probability generating function (of ${\displaystyle X}$) and ${\displaystyle M_X(t)}$ is the moment-generating function (of ${\displaystyle X}$).

Functions of independent random variables

Probability generating functions are particularly useful for dealing with functions of independent random variables. For example:

• If ${\displaystyle X_i,i=1,2,\cdots ,N}$ is a sequence of independent (and not necessarily identically distributed) random variables that take on natural-number values, and $${\displaystyle S_N=\sum _{i=1}^N{a}_i{X}_i,}$$ where the ${\displaystyle a_i}$ are constant natural numbers, then the probability generating function is given by $${\displaystyle G_{S_N}(z)=\mathrm{E}(z^{S_N})=\mathrm{E}\left(z^{\sum _{i=1}^N{a}_i{X}_i,}\right)=G_{X_1}(z^{a_1})G_{X_2}(z^{a_2})\cdots G_{X_N}(z^{a_N}).}$$
• In particular, if ${\displaystyle X}$ and ${\displaystyle Y}$ are independent random variables: $${\displaystyle G_{X+Y}(z)=G_X(z)\cdot G_Y(z)}$$ and $${\displaystyle G_{X-Y}(z)=G_X(z)\cdot G_Y(1/z).}$$
• In the above, the number ${\displaystyle N}$ of independent random variables in the sequence is fixed. Assume ${\displaystyle N}$ is discrete random variable taking values on the non-negative integers, which is independent of the ${\displaystyle X_i}$, and consider the probability generating function ${\displaystyle G_N}$. If the ${\displaystyle X_i}$ are not only independent but also identically distributed with common probability generating function ${\displaystyle G_X=G_{X_i}}$, then $${\displaystyle G_{S_N}(z)=G_N(G_X(z)).}$$ This can be seen, using the law of total expectation, as follows: $${\displaystyle \begin{array}{cc}{G}_{S_N}(z)& =\mathrm{E}(z^{S_N})=\mathrm{E}(z^{\sum _{i=1}^N{X}_i})\\ & =\mathrm{E}(\mathrm{E}(z^{\sum _{i=1}^N{X}_i}\mid N))=\mathrm{E}((G_X(z){)}^N)=G_N(G_X(z)).\end{array}}$$ This last fact is useful in the study of Galton–Watson processes and compound Poisson processes.
• When the ${\displaystyle X_i}$ are not supposed identically distributed (but still independent and independent of ${\displaystyle N}$), we have $${\displaystyle G_{S_N}(z)=\sum _{n\ge 1}{f}_n\prod _{i=1}^n{G}_{X_i}(z),}$$ where ${\displaystyle f_n=Pr(N=n).}$ For identically distributed ${\displaystyle X_i}$s, this simplifies to the identity stated before, but the general case is sometimes useful to obtain a decomposition of ${\displaystyle S_N}$ by means of generating functions.

Examples

• The probability generating function of an almost surely constant random variable, i.e. one with ${\displaystyle Pr(X=c)=1}$ and ${\displaystyle Pr(X\ne c)=0}$ is $${\displaystyle G(z)=z^c.}$$
• The probability generating function of a binomial random variable, the number of successes in ${\displaystyle n}$ trials, with probability ${\displaystyle p}$ of success in each trial, is $${\displaystyle G(z)={\left[(1-p)+pz\right]}^n.}$$ Note: it is the ${\displaystyle n}$-fold product of the probability generating function of a Bernoulli random variable with parameter ${\displaystyle p}$. So the probability generating function of a fair coin, is $${\displaystyle G(z)=\frac{1}{2}+\frac{z}{2}.}$$
• The probability generating function of a negative binomial random variable on ${\displaystyle \{0,1,2\cdots \}}$, the number of failures until the ${\displaystyle r^{th}}$ success with probability of success in each trial ${\displaystyle p}$, is $${\displaystyle G(z)={\left(\frac{p}{1-(1-p)z}\right)}^r,}$$ which converges for ${\displaystyle |z|<\frac{1}{1-p}}$. Note that this is the ${\displaystyle r}$-fold product of the probability generating function of a geometric random variable with parameter ${\displaystyle 1-p}$ on ${\displaystyle \{0,1,2,\cdots \}}$.
• The probability generating function of a Poisson random variable with rate parameter ${\displaystyle \lambda }$ is $${\displaystyle G(z)=e^{\lambda (z-1)}.}$$

Related concepts

The probability generating function is an example of a generating function of a sequence: see also formal power series. It is equivalent to, and sometimes called, the z-transform of the probability mass function.

Other generating functions of random variables include the moment-generating function, the characteristic function and the cumulant generating function. The probability generating function is also equivalent to the factorial moment generating function, which as ${\displaystyle \mathrm{E}\left[z^X\right]}$ can also be considered for continuous and other random variables.