Undergrad Proof to the Expression of Poisson Distribution

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SUMMARY

The Poisson distribution describes the probability of a given number of events occurring in a fixed interval of time, under specific conditions: events occur independently, the rate of occurrence is constant, and two events cannot occur simultaneously. The probability expression for a Poisson random variable X, representing the number of events, is derived as a limiting case of the binomial distribution. The derivation involves defining the probability of one event in a small interval and using the limit of binomial probabilities as the number of trials approaches infinity. This results in the Poisson cumulative distribution function (CDF).

PREREQUISITES
  • Understanding of Poisson distribution properties
  • Familiarity with binomial distribution concepts
  • Knowledge of limits in calculus
  • Basic probability theory
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  • Study the derivation of the Poisson distribution from the binomial distribution
  • Learn about the applications of the Poisson distribution in real-world scenarios
  • Explore the relationship between Poisson processes and exponential distributions
  • Investigate the use of Poisson regression in statistical modeling
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Anakin Skywalker
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Hello.
Given a range of time in which an event can occur an indefinite number of times, we say a random variable X folows a poisson distribution when it follows this statements:
  • X is the number of times an event occurs in an interval and X can take values 0, 1, 2, …
  • The occurrence of one event does not affect the probability that a second event will occur. That is, events occur independently.
  • The rate at which events occur is constant. The rate cannot be higher in some intervals and lower in other intervals.
  • Two events cannot occur at exactly the same instant.
  • The probability of an event in an interval is proportional to the length of the interval.
And in this case the probability related to x is given by the expression below:
poisson-formula.png


I would like to know how this expression is deduced.

P.S.: I used the informations in the wikipedia's page, so I'm not so sure that these topics are right.
https://en.wikipedia.org/wiki/Poiss..._Poisson_distribution_an_appropriate_model.3F
 
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The Poisson distribution can be deduced as a limiting case of the binomial distribution.

Let the probability of exactly one event occurring in a period of length ##\delta t## be ##g(\delta t)##, and define
$$\lambda\triangleq \lim_{\delta t\to 0}\frac{g(\delta t)}{\delta t}$$

Then if ##X(T)## is the number of events occurring in a period of length ##T## and ##n## is a positive integer, we have
$$Pr(X(T)\leq x)=Pr (B_n\leq x)$$
where ##B_n## is a binomial random variable with parameters ##(n,g(T/n))##.

Hence ##Pr (B_n\leq x)## is constant over ##n## and hence its limit as ##n\to\infty## exists and is equal to ##Pr(X(T)\leq x)##.

But the number of events occurring in a period of length ##T## is also equal to the Poisson random variable ##Y(\lambda,T)## for the number of events in time period ##T## with frequency ##\lambda##.

So we have
$$Pr(Y(\lambda,T)\leq x)=\lim_{n\to\infty}Pr(B_n\leq x)$$
and it can be shown that the expression on the RHS is equal to the formula for the Poisson CDF.

I've left some steps out because I'm a bit rushed this morning, but this should at least give an idea of how it's done.
 
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