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!}}
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{{infobox
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|title=Poisson distribution
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|above=<span style="font-size:1.5em;">{{M|X\sim\text{Poi}(\lambda)}}</span>
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|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)''
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|image=file.png
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|header1=Definition
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|label1=Type
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|data1=[[Discrete probability distribution|Discrete]], over {{M|\mathbb{N}_{\ge 0} }}
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|label2=[[Probability mass function|p.m.f]]
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|data2={{MM|\mathbb{P}[X\eq k]:\eq e^{-\lambda}\frac{\lambda^k}{k!} }}
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|label3=[[Cumulative distribution function|c.d.f]]
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|data3={{MM|\mathbb{P}[X\le k]\eq e^{-\lambda}\sum^k_{i\eq 0}\frac{\lambda^i}{k!} }}
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|header10=Characteristics
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|label12=[[Expected value]]
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|data12={{M|\mathbb{E}[X]\eq\lambda}}
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|label13=[[Variance]]
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|data13={{M|\text{Var}(X)\eq\lambda}}
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}}
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==Definition==
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* {{M|X\sim\text{Poisson}(\lambda)}}
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** for {{M|k\in\mathbb{N}_{\ge 0} }} we have: {{MM|\mathbb{P}[X\eq k]:\eq\frac{e^{-\lambda}\lambda^k}{k!} }}
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*** 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
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** 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}}
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==Mean==
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* {{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]}}
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*: {{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]}}
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*:* But! {{MM|e^x\eq\lim_{n\rightarrow\infty}\left(\sum^n_{i\eq 0}\frac{x^i}{i!}\right)}}
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** So {{MM|\eq\lambda e^{-\lambda} e^\lambda}}
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*** {{M|\eq\lambda}}
 
==Derivation==
 
==Derivation==
 
Standard Poisson distribution:
 
Standard Poisson distribution:

Revision as of 06:53, 20 September 2017

Stub grade: A*
This page is a stub
This page is a stub, so it contains little or minimal information and is on a to-do list for being expanded.The message provided is:
My informal derivation feels too formal, but isn't formal enough to be a formal one! Work in progress!
Poisson distribution
XPoi(λ)
λR0
(λ - the average rate of events per unit)
file.png
Definition
Type Discrete, over N0
p.m.f P[X=k]:=eλλkk!
c.d.f P[Xk]=eλki=0λik!
Characteristics
Expected value E[X]=λ
Variance Var(X)=λ

Definition

  • XPoisson(λ)
    • for kN0 we have: P[X=k]:=eλλkk!
      • the first 2 terms are easy to give: eλ and λeλ respectively, after that we have 12λ2eλ and so forth
    • for kN0 we have: P[Xk]=eλkj=01j!λj


Mean

  • n=0n×P[X=n]=n=0[n×eλλnn!]=
    0+[eλn=1λn(n1)!]
    =eλλ[n=1λn1(n1)!]
    =λeλ[n=0λnn!]
    =λeλ[limn(nk=0λkk!)]
    • But! ex=limn(ni=0xii!)
    • So =λeλeλ
      • =λ

Derivation

Standard Poisson distribution:

  • Let S:=[0,1)R, recall that means S={xR | 0x<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 NN1

  • Let Si,N:=[i1N,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 [i1N,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](1λN)
    • 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(Ni=1C(Si,N))=limN(Bin(N,λN))


We see:

  • P[C(S)=k]=limN
    (P[Bin(N,λN)=k])=limN(NCk (λN)k(1λN)Nk)

We claim that:

  • limN(NCk (λN)k(1λN)Nk)=λkk!eλ

We will tackle this in two parts:

  • limN(NCk (λN)kA (1λN)NkB)
    where Beλ and Aλkk!

Proof

Key notes:

A

Notice:

  • NCk (λN)k=N!(Nk)!k!1Nkλk
    =1k!k termsN(N1)(Nk+2)(Nk+1)NNNk timesλk
    • Notice that as N gets bigger Nk+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!

B

This comes from:

  • ex:=limn((1+xn)n)
    , so we get the eλ term.

Notes

  1. Jump up Recall again that means {xR | i1Nx<iN}