Math 2.0

definition

Probability-generating function

Definition

Let XX be a random variable with values in {0,1,2,… }\{0, 1, 2, \dots\} and with mass function p(k)=P(X=k)p(k) = P(X = k). The probability-generating function of XX is GX(z)=E[zX]=∑k=0∞p(k) zk,G_X(z) = E\left[z^X\right] = \sum_{k=0}^{\infty} p(k) \, z^k, for each complex zz with ∣z∣≤1|z| \le 1.

Remarks

GXG_X is the generating function of the sequence p(0),p(1),…p(0), p(1), \dots. The series converges absolutely for ∣z∣≤1|z| \le 1, and GX(1)=1G_X(1) = 1. GXG_X determines the law of XX: P(X=k)=GX(k)(0)/k!P(X = k) = G_X^{(k)}(0)/k!. If E[X]<∞E[X] < \infty, then E[X]=GX′(1)E[X] = G_X'(1), with the left derivative at 11. Also E[X(X−1)]=GX′′(1)E[X(X - 1)] = G_X''(1) when it is finite. For X∼Poisson⁡(λ)X \sim \operatorname{Poisson}(\lambda), GX(z)=eλ(z−1)G_X(z) = e^{\lambda(z - 1)}. For X∼Bin⁡(n,p)X \sim \operatorname{Bin}(n, p), GX(z)=(1−p+pz)nG_X(z) = (1 - p + pz)^n.

Explanations

L1 human

The probability-generating function packs all the probabilities of a count into one function. Derivatives of this function at 00 give back the probabilities, and derivatives at 11 give the moments.

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Sources

  1. Web https://en.wikipedia.org/wiki/Probability_generating_function, Univariate case