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Në teorinë e probabilitetit , ligji i ngjarjeve të rralla ose teorema Poisson e kufirit shpall se shpërndarja Poisson mund të përdoret si një përafrim i shpërndarjes binomiale, nën kushte të veçanta.[ 1] Teorema u emërtua sipas Siméon Denis Poisson (1781–1840). Një përgjithësim i saj është teorema Le Cam.
Le të jetë
p
n
{\displaystyle p_{n}}
të jetë një sekuencë e numrave realë në
[
0
,
1
]
{\displaystyle [0,1]}
të tillë që sekuenca
n
p
n
{\displaystyle np_{n}}
konvergjon në një kufi të fundëm
λ
{\displaystyle \lambda }
. Atëherë:
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{\displaystyle \lim _{n\to \infty }{n \choose k}p_{n}^{k}(1-p_{n})^{n-k}=e^{-\lambda }{\frac {\lambda ^{k}}{k!}}}
lim
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…
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{\displaystyle {\begin{aligned}\lim \limits _{n\rightarrow \infty }{n \choose k}p_{n}^{k}(1-p_{n})^{n-k}&\simeq \lim _{n\to \infty }{\frac {n(n-1)(n-2)\dots (n-k+1)}{k!}}\left({\frac {\lambda }{n}}\right)^{k}\left(1-{\frac {\lambda }{n}}\right)^{n-k}\\&=\lim _{n\to \infty }{\frac {n^{k}+O\left(n^{k-1}\right)}{k!}}{\frac {\lambda ^{k}}{n^{k}}}\left(1-{\frac {\lambda }{n}}\right)^{n-k}\\&=\lim _{n\to \infty }{\frac {\lambda ^{k}}{k!}}\left(1-{\frac {\lambda }{n}}\right)^{n-k}.\end{aligned}}}
Meqënëse
lim
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{\displaystyle \lim _{n\to \infty }\left(1-{\frac {\lambda }{n}}\right)^{n}=e^{-\lambda }}
dhe
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{\displaystyle \lim _{n\to \infty }\left(1-{\frac {\lambda }{n}}\right)^{-k}=1,}
kjo jep
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{\displaystyle {n \choose k}p^{k}(1-p)^{n-k}\simeq {\frac {\lambda ^{k}e^{-\lambda }}{k!}}.}
Duke përdorur përafrimin e Stirlingut , mund të shkruhet:
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{\displaystyle {\begin{aligned}{n \choose k}p^{k}(1-p)^{n-k}&={\frac {n!}{(n-k)!k!}}p^{k}(1-p)^{n-k}\\&\simeq {\frac {{\sqrt {2\pi n}}\left({\frac {n}{e}}\right)^{n}}{{\sqrt {2\pi \left(n-k\right)}}\left({\frac {n-k}{e}}\right)^{n-k}k!}}p^{k}(1-p)^{n-k}\\&={\sqrt {\frac {n}{n-k}}}{\frac {n^{n}e^{-k}}{\left(n-k\right)^{n-k}k!}}p^{k}(1-p)^{n-k}.\end{aligned}}}
Nëse
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{\displaystyle n\to \infty }
dhe
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{\displaystyle np=\lambda }
:
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{\displaystyle {\begin{aligned}{n \choose k}p^{k}(1-p)^{n-k}&\simeq {\frac {n^{n}\,p^{k}(1-p)^{n-k}e^{-k}}{\left(n-k\right)^{n-k}k!}}\\&={\frac {n^{n}\left({\frac {\lambda }{n}}\right)^{k}\left(1-{\frac {\lambda }{n}}\right)^{n-k}e^{-k}}{n^{n-k}\left(1-{\frac {k}{n}}\right)^{n-k}k!}}\\&={\frac {\lambda ^{k}\left(1-{\frac {\lambda }{n}}\right)^{n-k}e^{-k}}{\left(1-{\frac {k}{n}}\right)^{n-k}k!}}\\&\simeq {\frac {\lambda ^{k}\left(1-{\frac {\lambda }{n}}\right)^{n}e^{-k}}{\left(1-{\frac {k}{n}}\right)^{n}k!}}.\end{aligned}}}
Meqë
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{\displaystyle n\to \infty }
,
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{\displaystyle \left(1-{\frac {x}{n}}\right)^{n}\to e^{-x}}
kështu që:
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{\displaystyle {\begin{aligned}{n \choose k}p^{k}(1-p)^{n-k}&\simeq {\frac {\lambda ^{k}e^{-\lambda }e^{-k}}{e^{-k}k!}}\\&={\frac {\lambda ^{k}e^{-\lambda }}{k!}}\end{aligned}}}
^ Papoulis, Athanasios ; Pillai, S. Unnikrishna . Probability, Random Variables, and Stochastic Processes (bot. 4th).