Poisson limit theorem

{{Short description|Probability Theory}}

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File:Binomial versus poisson.svg (black lines) and the binomial distribution with {{nobr| {{mvar|n}} {{=}} 10 }} (red circles), {{nobr| {{mvar|n}} {{=}} 20 }} (blue circles), {{nobr| {{mvar|n}} {{=}} 1000 }} (green circles). All distributions have a mean of 5. The horizontal axis shows the number of events {{mvar|k}}. As {{mvar|n}} gets larger, the Poisson distribution becomes an increasingly better approximation for the binomial distribution with the same mean.]]

In probability theory, the law of rare events or Poisson limit theorem states that the Poisson distribution may be used as an approximation to the binomial distribution, under certain conditions.{{cite book|author1=Papoulis, Athanasios|author2=Pillai, S. Unnikrishna|authorlink1=Athanasios Papoulis|authorlink2=Unnikrishna Pillai|title=Probability, Random Variables, and Stochastic Processes|edition=4th}} The theorem was named after Siméon Denis Poisson (1781–1840). A generalization of this theorem is Le Cam's theorem.

{{Broader|Poisson distribution#Law of rare events}}

Theorem

Let p_n be a sequence of real numbers in [0,1] such that the sequence n p_n converges to a finite limit \lambda . Then:

:\lim_{n\to \infty} {n \choose k} p_n^k (1-p_n)^{n-k} = e^{-\lambda}\frac{\lambda^k}{k!}

First proof

Assume \lambda > 0 (the case \lambda = 0 is easier). Then

:

\begin{align}

\lim\limits_{n\rightarrow\infty}{n \choose k} p_n^k (1-p_n)^{n-k}

&= \lim_{n\to\infty}\frac{n(n-1)(n-2)\dots(n-k+1)}{k!} \left(\frac{\lambda}{n}(1+o(1))\right)^k \left(1- \frac{\lambda}{n}(1+o(1))\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}(1+o(1))\right)^{n} \left(1- \frac{\lambda}{n}(1+o(1))\right)^{-k}\\

&= \lim_{n\to\infty}\frac{\lambda^k}{k!} \left(1-\frac{\lambda}{n}(1+o(1))\right)^{n}.

\end{align}

Since

: \lim_{n\to\infty} \left(1-\frac{\lambda}{n}(1+o(1))\right)^{n} = e^{-\lambda}

this leaves

:{n \choose k}p^k (1-p)^{n-k} \simeq \frac{\lambda^k e^{-\lambda}}{k!}.

=Alternative proof=

Using Stirling's approximation, it can be written:

:

\begin{align}

{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{align}

Letting n \to \infty and np = \lambda:

:

\begin{align}

{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{align}

As n \to \infty, \left(1-\frac{x}{n}\right)^n \to e^{-x} so:

:\begin{align}

{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{align}

=Ordinary generating functions=

It is also possible to demonstrate the theorem through the use of ordinary generating functions of the binomial distribution:

:

G_\operatorname{bin}(x;p,N)

\equiv \sum_{k=0}^N \left[ \binom{N}{k} p^k (1-p)^{N-k} \right] x^k

= \Big[ 1 + (x-1)p \Big]^N

by virtue of the binomial theorem. Taking the limit N \rightarrow \infty while keeping the product pN\equiv\lambda constant, it can be seen:

:

\lim_{N\rightarrow\infty} G_\operatorname{bin}(x;p,N)

= \lim_{N\rightarrow\infty} \left[ 1 + \frac{\lambda(x-1)}{N} \right]^N

= \mathrm{e}^{\lambda(x-1)}

= \sum_{k=0}^{\infty} \left[ \frac{\mathrm{e}^{-\lambda}\lambda^k}{k!} \right] x^k

which is the OGF for the Poisson distribution. (The second equality holds due to the definition of the exponential function.)

See also

References