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# probability generating function

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(pgf)

For the discrete random variable X, with probability distribution P(X=xj), j=1, 2, 3,…, the probability-generating function G is defined by where t is an arbitrary variable. Note that G(t) is the expectation of tX and G(1)=1. If the set of possible values of x is infinite, |t| needs to be small enough for the series to converge.

If the first and second derivatives of G(t) with respect to t are denoted by G′(t) and G″(t), respectively, the expectation and variance of X are given by G′(1) and G″(1)+G′(1)−{G′(1)}2, where, for example, G′(1) denotes the value of G′(t) when t=1.

Like the moment-generating function the probability-generating function can provide a useful alternative description of a probability distribution. For example, if Y denotes the sum of n independent random variables, each having pgf G(t), then P(Y=y) is the coefficient of ty in {G(t)}n.

Another useful property is that, if X and Y are independent random variables with probability-generating functions GX(t) and GY(t), respectively, then the probability-generating function of Z=X+Y is GZ(t), where

GZ(t)=GX(t)×GY(t).

De Moivre used the probability-generating function technique in 1730. The term itself became common following its use by Bartlett in 1940. See also moment-generating function.

Subjects: Probability and Statistics.

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