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若随机变量 X X X服从参数为 p p p的几何分布, 则有
Pr ( X = k ) = ( 1 − p ) k p \Pr(X=k) = (1 - p)^kp Pr(X=k)=(1−p)kp
其中 k = 0 , 1 , ⋯ k=0, 1, \cdots k=0,1,⋯
E ( X ) = 1 − p p E(X) = \frac{1-p}{p} E(X)=p1−p
证明:
E ( X ) = ∑ k = 0 + ∞ k Pr ( X = k ) = ∑ k = 0 + ∞ k ( 1 − p ) k p E(X) = \sum_{k=0}^{+\infty}k\Pr(X=k) = \sum_{k=0}^{+\infty}k(1-p)^kp E(X)=∑k=0+∞kPr(X=k)=∑k=0+∞k(1−p)kp
= p ( 1 − p ) ∑ k = 0 + ∞ k ( 1 − p ) k − 1 =p(1-p)\sum_{k=0}^{+\infty}k(1-p)^{k-1} =p(1−p)∑k=0+∞k(1−p)k−1
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