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[limn→∞(n∑k=0λkk!)]
- But! ex=limn→∞(n∑i=0xii!)
- But! ex=limn→∞(n∑i=0xii!)
- So =λe−λeλ
- =λ
- =λe−λ[∞∑n=0λnn!]
Derivation
Standard Poisson distribution:
- Let S:=[0,1)⊆R, recall that means S={x∈R | 0≤x<1}
- Let λ 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∈N≥1
- Let Si,N:=[i−1N,iN)[Note 1] for i∈{1,…,N}⊆N
We will now define a random variable that counts the occurrences of events per interval.
- Let C(Si,N) be the RV such that its value is the number of times the event occurred in the [i−1N,iN) interval
We now require:
- limN→∞(P[C(Si,N)≥2])=0- such that:
- as the Si,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 Si,∞ so no BORV(λN) distribution then either
Note that:
- limN→∞(C(Si,N))=limN→∞(BORV(λN))
- This is supposed to convey that the distribution of C(Si,N) as N gets large gets arbitrarily close to BORV(λN)
So we may say for sufficiently large N that:
- C(Si,N)∼(approx)BORV(λN), so that:
- P[C(Si,N)=0]≈(1−λN)
- P[C(Si,N)=1]≈λN, and of course
- P[C(Si,N)≥2]≈0
Assuming the C(Si,N) are independent over i (which surely we get from the BORV distributions?) we see:
- C(S)∼(approx)Bin(N,λN)or, more specifically: C(S)=limN→∞(N∑i=1C(Si,N))=limN→∞(Bin(N,λN))
We see:
- P[C(S)=k]=limN→∞(P[Bin(N,λN)=k])=limN→∞(NCk (λN)k(1−λN)N−k)
We claim that:
- limN→∞(NCk (λN)k(1−λN)N−k)=λkk!e−λ
We will tackle this in two parts:
- limN→∞(NCk (λN)k⏟A (1−λN)N−k⏟B)where B→e−λ and A→λkk!
Proof
Key notes:
A
Notice:
- NCk (λN)k=N!(N−k)!k!⋅1Nk⋅λk
- =1k!⋅k terms⏞N(N−1)⋯(N−k+2)(N−k+1)N⋅N⋯N⏟k times⋅λk
- 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→∞) this giving:
- λkk!
- λkk!
- =1k!⋅k terms⏞N(N−1)⋯(N−k+2)(N−k+1)N⋅N⋯N⏟k times⋅λk
B
This comes from:
- ex:=limn→∞((1+xn)n), so we get the e−λ term.
Notes
- Jump up ↑ Recall again that means {x∈R | i−1N≤x<iN}