According to the Stirling's approximation:
n ! ≈ 2 π n ( n e ) n n!\approx\sqrt{2\pi n}(\frac{n}{e})^n n ! ≈ 2 πn ( e n ) n for large n
The mean of the binomial distribution μ = n p ⇒ p = μ n \mu=np\Rarr p=\frac{\mu}{n} μ = n p ⇒ p = n μ
P ( x = k ) = C n k p k ( 1 − p ) n − k = = n ! k ! ( n − k ) ! ( μ n ) k ( 1 − μ n ) n − k ≈ ≈ 2 π n ( n / e ) n 2 π ( n − k ) ( ( n − k ) / e ) n − k k ! μ k n k ( 1 − μ / n ) n ( 1 − μ / n ) k P(x=k)=C_n^kp^k(1-p)^{n-k}=\\
=\frac{n!}{k!(n-k)!}(\frac{\mu}{n})^k(1-\frac{\mu}{n})^{n-k}\approx\\
\approx\frac{\sqrt{2\pi n}(n/e)^n}{\sqrt{2\pi(n-k)}((n-k)/e)^{n-k}k!}\frac{\mu^k}{n^k}\frac{(1-\mu/n)^n}{(1-\mu/n)^{k}} P ( x = k ) = C n k p k ( 1 − p ) n − k = = k ! ( n − k )! n ! ( n μ ) k ( 1 − n μ ) n − k ≈ ≈ 2 π ( n − k ) (( n − k ) / e ) n − k k ! 2 πn ( n / e ) n n k μ k ( 1 − μ / n ) k ( 1 − μ / n ) n
Let's use some approximations for large n:
( 1 − μ n ) n ≈ e − μ ( 1 − μ n ) k ≈ 1 n n − k ≈ 1 ( n / e ) n ( ( n − k ) / e ) n − k ≈ e k n k ( 1 − k / n ) k ( 1 − k / n ) n ≈ e − k n k e − k = n k (1-\frac{\mu}{n})^n\approx e^{-\mu}\\
(1-\frac{\mu}{n})^k\approx1\\
\sqrt{\frac{n}{n-k}}\approx1\\
\frac{(n/e)^n}{((n-k)/e)^{n-k}}\approx\frac{e^kn^k(1-k/n)^k}{(1-k/n)^n}\approx\frac{e^{-k}n^k}{e^{-k}}=n^k ( 1 − n μ ) n ≈ e − μ ( 1 − n μ ) k ≈ 1 n − k n ≈ 1 (( n − k ) / e ) n − k ( n / e ) n ≈ ( 1 − k / n ) n e k n k ( 1 − k / n ) k ≈ e − k e − k n k = n k
P ( k ) ≈ n k μ k n k k ! e − μ = μ k e − μ k ! P(k)\approx n^k\frac{\mu^k}{n^kk!}e^{-\mu}=\frac{\mu^ke^{-\mu}}{k!} P ( k ) ≈ n k n k k ! μ k e − μ = k ! μ k e − μ - Poisson's distribution.
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