PGFs of Standard Distributions (Edexcel A-Level Further Mathematics): Flashcards

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PGFs of Standard Distributions
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General PGF definition formula

GX(t)=∑x=0∞P(X=x)txG_X(t) = \sum_{x=0}^\infty P(X = x) t^x

PGF for X∼B(n,p)X \sim B(n, p)

GX(t)=[pt+(1−p)]nG_X(t) = [pt + (1-p)]^n

PGF for X∼Po(λ)X \sim \text{Po}(\lambda)

GX(t)=eλ(t−1)G_X(t) = e^{\lambda(t-1)}

PGF for X∼Geom(p)X \sim \text{Geom}(p)

GX(t)=p1−(1−p)tG_X(t) = \frac{p}{1 - (1-p)t}

PGF for X∼NegBin(r,p)X \sim \text{NegBin}(r, p)

GX(t)=(p1−(1−p)t)rG_X(t) = \left(\frac{p}{1 - (1-p)t}\right)^r

Mean from PGF

E[X]=GX′(1)\mathbb{E}[X] = G_X'(1)

Variance from PGF

Var(X)=GX′′(1)+GX′(1)−[GX′(1)]2\text{Var}(X) = G_X''(1) + G_X'(1) - [G_X'(1)]^2

Mean of X∼B(n,p)X \sim B(n, p)

E[X]=np\mathbb{E}[X] = np

Variance of X∼Po(λ)X \sim \text{Po}(\lambda)

Var(X)=λ\text{Var}(X) = \lambda

Convergence condition for Geom/NegBin PGFs

∣t∣<11−p|t| < \frac{1}{1-p}

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