Poisson limit theorem explained

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.^[1] The theorem was named after Siméon Denis Poisson (1781 - 1840). A generalization of this theorem is Le Cam's theorem.

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 λ . Then:\lim _{n\to} {n \choose k} p ^{k} _{n} ( 1- p ^{n - k} _{n)} = e ^{-λ} (λ ^{k})/(k!)

First proof

Assume

λ > 0 (the case λ = 0 is easier). Then\begin{align} \lim\limits _{n → infty} {n \choose k} p ^{k} _{n} ( 1- p ^{n - k} _{n)} & = \lim _{n\toinfty} (n(n -1 )(n -2 ) ... (n - k +1 ))/(\left( k!) (λ)/(n) ( 1+ o( 1 ))\right) ^{k} \left( 1- (λ)/(n) ( 1+ o( 1 ))\right) ^{n-k} \\ & = \lim _{n\toinfty} (n ^{k+O\left(n} ^{k-1} \right))/(k!) (λ ^{k})/(n ^{k}) \left( 1- (λ)/(n) ( 1+ o( 1 ))\right) ^{n} \left( 1- (λ)/(n) ( 1+ o( 1 ))\right) ^{-k} \\ & = \lim _{n\toinfty} (λ ^{k})/(\left( 1- k!) (λ)/(n) ( 1+ o( 1 ))\right) ^{n} . \end{align}

Since

\lim _{n\toinfty} \left( 1- (λ)/(n) ( 1+ o( 1 ))\right) ^{n} = e ^{-λ}

this leaves

{n \choose k}p ^{k} ( 1- p) ^{n-k} \simeq (λ ^{k} e ^{-λ})/(k!) .

Alternative proof

Using Stirling's approximation, it can be written:

\begin{align} {n \choose k}p ^{k} ( 1- p) ^{n-k} & = (n!)/((n - k)!k!) p ^{k} ( 1- p) ^{n-k} \ &\simeq (\sqrt{ 2 \pi n)/(\left( (n)/(e)) \right) ^{n}{} \sqrt{ 2 \pi \left(n - k\right)}\left( (n - k)/(e) \right) ^{n-k} k!} p ^{k} ( 1- p) ^{n-k} \ & = \sqrt{ (n)/(n - k) }\fracp^k (1-p)^.\end

Letting

n \to inf ty and np = λ :\begin{align} {n \choose k}p ^{k} ( 1- p) ^{n-k} &\simeq (n ^{np} ^{k} ( 1- p) ^{n-k} e ^{-k})/(\left(n - k\right) ^{n-k} k!) \\& = (n ^{n\left( (λ)/(n)} \right) ^{k} \left( 1- (λ)/(n) \right) ^{n-k} e ^{-k})/(n ^{n - k} \left( 1- (k)/(n) \right) ^{n-k} k!) \\& = (λ ^{k} \left( 1- (λ)/(n) \right) ^{n-k} e ^{-k})/(\left( 1- (k)/(\right) ^{n-k} k! n)) \ &\simeq (λ ^{k} \left( 1- (λ)/(n) \right) ^{n} e ^{-k})/(\left( 1- (k)/(\right) ^{n} k! n)) . \end{align}

As

n \to inf ty , \left( 1- (x)/(n) \right) ^{n} \to e ^{-x} so:\begin{align} {n \choose k}p ^{k} ( 1- p) ^{n-k} &\simeq (λ ^{k} e ^{-λ} e ^{-k})/(e ^{-k} k!) \\& = (λ ^{k} e ^{-λ})/(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 ^{N} _{k =0} \left[ \binom{N}{k} p ^{k} ( 1- p) ^{N-k} \right] x ^{k} = [ 1 + (x -1 )p ] ^{N}

by virtue of the binomial theorem. Taking the limit

N → inf ty while keeping the product pN\equiv λ constant, it can be seen:\lim _{N → infty} G _{\operatorname{bin}(x;p,N)} = \lim _{N → infty} \left[ 1 + (λ (x -1 ))/(N) \right] ^{N} = e ^{λ(x-1)} = \sum ^{inf ty} _{k =0} \left[ (e ^{-λ} λ ^{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

• De Moivre–Laplace theorem
• Le Cam's theorem

Notes and References

1. Book: Papoulis, Athanasios. Pillai, S. Unnikrishna. Athanasios Papoulis. Unnikrishna Pillai. Probability, Random Variables, and Stochastic Processes. 4th.