Difference between revisions of "Poisson distribution"
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(Created page with "{{Stub page|grade=A*|msg=My informal derivation feels too formal, but isn't formal enough to be a formal one! Work in progress!}} ==Derivation== Standard Poisson distribution:...") |
(Added infobox, early definition, mean claim - very sketchy but done none the less.) |
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{{Stub page|grade=A*|msg=My informal derivation feels too formal, but isn't formal enough to be a formal one! Work in progress!}} | {{Stub page|grade=A*|msg=My informal derivation feels too formal, but isn't formal enough to be a formal one! Work in progress!}} | ||
+ | {{infobox | ||
+ | |title=Poisson distribution | ||
+ | |above=<span style="font-size:1.5em;">{{M|X\sim\text{Poi}(\lambda)}}</span> | ||
+ | |subheader=<span style="font-size:1.2em;">{{M|\lambda\in\mathbb{R}_{\ge 0} }}</span><br/>''({{M|\lambda}} - the average rate of events per unit)'' | ||
+ | |image=file.png | ||
+ | |header1=Definition | ||
+ | |label1=Type | ||
+ | |data1=[[Discrete probability distribution|Discrete]], over {{M|\mathbb{N}_{\ge 0} }} | ||
+ | |label2=[[Probability mass function|p.m.f]] | ||
+ | |data2={{MM|\mathbb{P}[X\eq k]:\eq e^{-\lambda}\frac{\lambda^k}{k!} }} | ||
+ | |label3=[[Cumulative distribution function|c.d.f]] | ||
+ | |data3={{MM|\mathbb{P}[X\le k]\eq e^{-\lambda}\sum^k_{i\eq 0}\frac{\lambda^i}{k!} }} | ||
+ | |header10=Characteristics | ||
+ | |label12=[[Expected value]] | ||
+ | |data12={{M|\mathbb{E}[X]\eq\lambda}} | ||
+ | |label13=[[Variance]] | ||
+ | |data13={{M|\text{Var}(X)\eq\lambda}} | ||
+ | }} | ||
+ | ==Definition== | ||
+ | * {{M|X\sim\text{Poisson}(\lambda)}} | ||
+ | ** for {{M|k\in\mathbb{N}_{\ge 0} }} we have: {{MM|\mathbb{P}[X\eq k]:\eq\frac{e^{-\lambda}\lambda^k}{k!} }} | ||
+ | *** the first 2 terms are easy to give: {{M|e^{\lambda} }} and {{M|\lambda e^{-\lambda} }} respectively, after that we have {{M|\frac{1}{2}\lambda^2 e^{-\lambda} }} and so forth | ||
+ | ** for {{M|k\in\mathbb{N}_{\ge 0} }} we have: {{MM|\mathbb{P}[X\le k]\eq e^{-\lambda}\sum^k_{j\eq 0}\frac{1}{j!}\lambda^j}} | ||
+ | |||
+ | |||
+ | ==Mean== | ||
+ | * {{MM|\sum^\infty_{n\eq 0} n\times\mathbb{P}[X\eq n]\eq\sum^\infty_{n\eq 0}\left[ n\times e^{-\lambda}\frac{\lambda^n}{n!}\right]\eq}}{{MM|0+\left[ e^{-\lambda}\sum^\infty_{n\eq 1} \frac{\lambda^n}{(n-1)!}\right] }}{{MM|\eq e^{-\lambda}\lambda\left[\sum^\infty_{n\eq 1}\frac{\lambda^{n-1} }{(n-1)!}\right]}} | ||
+ | *: {{MM|\eq \lambda e^{-\lambda}\left[\sum^{\infty}_{n\eq 0}\frac{\lambda^n}{n!}\right]}}{{MM|\eq \lambda e^{-\lambda}\left[\lim_{n\rightarrow\infty}\left(\sum^{n}_{k\eq 0}\frac{\lambda^k}{k!}\right)\right]}} | ||
+ | *:* But! {{MM|e^x\eq\lim_{n\rightarrow\infty}\left(\sum^n_{i\eq 0}\frac{x^i}{i!}\right)}} | ||
+ | ** So {{MM|\eq\lambda e^{-\lambda} e^\lambda}} | ||
+ | *** {{M|\eq\lambda}} | ||
==Derivation== | ==Derivation== | ||
Standard Poisson distribution: | Standard Poisson distribution: |
Revision as of 06:53, 20 September 2017
Stub grade: A*
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My informal derivation feels too formal, but isn't formal enough to be a formal one! Work in progress!
Poisson distribution | |
X∼Poi(λ) | |
λ∈R≥0 (λ - the average rate of events per unit) | |
file.png | |
Definition | |
---|---|
Type | Discrete, over N≥0 |
p.m.f | P[X=k]:=e−λλkk! |
c.d.f | P[X≤k]=e−λk∑i=0λik! |
Characteristics | |
Expected value | E[X]=λ |
Variance | Var(X)=λ |
Contents
[hide]Definition
- X∼Poisson(λ)
- for k∈N≥0 we have: P[X=k]:=e−λλkk!
- the first 2 terms are easy to give: eλ and λe−λ respectively, after that we have 12λ2e−λ and so forth
- for k∈N≥0 we have: P[X≤k]=e−λk∑j=01j!λj
- for k∈N≥0 we have: P[X=k]:=e−λλkk!
Mean
- ∞∑n=0n×P[X=n]=∞∑n=0[n×e−λλnn!]=0+[e−λ∞∑n=1λn(n−1)!]=e−λλ[∞∑n=1λn−1(n−1)!]
- =λe−λ[∞∑n=0λnn!]=λe−λ[lim
- But! e^x\eq\lim_{n\rightarrow\infty}\left(\sum^n_{i\eq 0}\frac{x^i}{i!}\right)
- So \eq\lambda e^{-\lambda} e^\lambda
- \eq\lambda
- =λe−λ[∞∑n=0λnn!]=λe−λ[lim
Derivation
Standard Poisson distribution:
- Let S:\eq[0,1)\subseteq\mathbb{R} , recall that means S\eq\{x\in\mathbb{R}\ \vert\ 0\le x<1\}
- Let \lambda be the average count of some event that can occur 0 or more times on S
We will now divide S up into N equally sized chunks, for N\in\mathbb{N}_{\ge 1}
- Let S_{i,N}:\eq\left[\frac{i-1}{N},\frac{i}{N}\right)[Note 1] for i\in\{1,\ldots,N\}\subseteq\mathbb{N}
We will now define a random variable that counts the occurrences of events per interval.
- Let C\big(S_{i,N}\big) be the RV such that its value is the number of times the event occurred in the \left[\frac{i-1}{N},\frac{i}{N}\right) interval
We now require:
- \lim_{N\rightarrow\infty}\left(\mathbb{P}[C\big(S_{i,N}\big)\ge 2]\right)\eq 0 - such that:
- as the S_{i,N} get smaller the chance of 2 or more events occurring in the space reaches zero.
- Warning:This is phrased as a limit, I'm not sure it should be as we don't have any S_{i,\infty} so no \text{BORV}(\frac{\lambda}{N}) distribution then either
Note that:
- \lim_{N\rightarrow\infty}\big(C(S_{i,N})\big)\eq\lim_{N\rightarrow\infty}\left(\text{BORV}\left(\frac{\lambda}{N}\right)\right)
- This is supposed to convey that the distribution of C(S_{i,N}) as N gets large gets arbitrarily close to \text{BORV}(\frac{\lambda}{N})
So we may say for sufficiently large N that:
- C(S_{i,N})\mathop{\sim}_{\text{(approx)} } \text{BORV}(\frac{\lambda}{N}), so that:
- \mathbb{P}[C(S_{i,N})\eq 0]\approx(1-\frac{\lambda}{N})
- \mathbb{P}[C(S_{i,N})\eq 1]\approx \frac{\lambda}{N} , and of course
- \mathbb{P}[C(S_{i,N})\ge 2]\approx 0
Assuming the C(S_{i,N}) are independent over i (which surely we get from the \text{BORV} distributions?) we see:
- C(S)\mathop{\sim}_{\text{(approx)} } \text{Bin} \left(N,\frac{\lambda}{N}\right) or, more specifically: C(S)\eq\lim_{N\rightarrow\infty}\Big(\sum^N_{i\eq 1}C(S_{i,N})\Big)\eq\lim_{N\rightarrow\infty}\left(\text{Bin}\left(N,\frac{\lambda}{N}\right)\right)
We see:
- \mathbb{P}[C(S)\eq k]\eq\lim_{N\rightarrow\infty} \Big(\mathbb{P}\big[\text{Bin}(N,\frac{\lambda}{N})\eq k\big]\Big)\eq\lim_{N\rightarrow\infty}\left({}^N\!C_k\ \left(\frac{\lambda}{N}\right)^k\left(1-\frac{\lambda}{N}\right)^{N-k}\right)
We claim that:
- \lim_{N\rightarrow\infty}\left({}^N\!C_k\ \left(\frac{\lambda}{N}\right)^k\left(1-\frac{\lambda}{N}\right)^{N-k}\right)\eq \frac{\lambda^k}{k!}e^{-\lambda}
We will tackle this in two parts:
- \lim_{N\rightarrow\infty}\Bigg(\underbrace{ {}^N\!C_k\ \left(\frac{\lambda}{N}\right)^k}_{A}\ \underbrace{\left(1-\frac{\lambda}{N}\right)^{N-k} }_{B}\Bigg) where B\rightarrow e^{-\lambda} and A\rightarrow \frac{\lambda^k}{k!}
Proof
Key notes:
A
Notice:
- {}^N\!C_k\ \left(\frac{\lambda}{N}\right)^k \eq \frac{N!}{(N-k)!k!}\cdot\frac{1}{N^k}\cdot\lambda^k
- \eq\frac{1}{k!}\cdot\frac{\overbrace{N(N-1)\cdots(N-k+2)(N-k+1)}^{k\text{ terms} } }{\underbrace{N\cdot N\cdots N}_{k\text{ times} } } \cdot\lambda^k
- Notice that as N gets bigger N-k+1 is "basically" N so the Ns in the denominator cancel (in fact the value will be slightly less than 1, tending towards 1 as N\rightarrow\infty) this giving:
- \frac{\lambda^k}{k!}
B
This comes from:
- e^x:\eq\lim_{n\rightarrow\infty}\left(\left(1+\frac{x}{n}\right)^n\right), so we get the e^{-\lambda} term.
Notes
- Jump up ↑ Recall again that means \{x\in\mathbb{R}\ \vert\ \frac{i-1}{N}\le x < \frac{i}{N} \}