Are Poisson and Uniform Distributions Paradoxical on a Finite Line?

In summary: The Poisson distribution gives the waiting time until the next event.I think he means, distribute points randomly along an interval such that their _waiting times_ are distributed as Poisson random variables.
  • #1
benjaminmar8
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0
Hi, all,

Let's say we deploy some random points on a line of finite length according to a poisson distribution of density \lambda. Can I say that these points are also "uniformly" distributed on the same line?

thks
 
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  • #2
benjaminmar8 said:
Hi, all,

Let's say we deploy some random points on a line of finite length according to a poisson distribution of density \lambda. Can I say that these points are also "uniformly" distributed on the same line?

thks
No, you can't. (And there is no "paradox".) With Poisson distribution, with parameter [itex]\lambda[/itex], points are as likely to be [itex]\le \lamba[/itex] as larger. With a "uniform" distribution, they are as likely to be less than or equal to the midpoint of the interval as to be above it. Further, points in a Poisson distribution are more likely to be close to \lambda than not while there is no number in an interval that points in a uniform distribution are more likely to be close to.
 
  • #3
HallsofIvy said:
No, you can't. (And there is no "paradox".) With Poisson distribution, with parameter [itex]\lambda[/itex], points are as likely to be [itex]\le \lamba[/itex] as larger. With a "uniform" distribution, they are as likely to be less than or equal to the midpoint of the interval as to be above it. Further, points in a Poisson distribution are more likely to be close to \lambda than not while there is no number in an interval that points in a uniform distribution are more likely to be close to.

The Poisson distribution gives the waiting time until the next event. I think he means, distribute points randomly along an interval such that their _waiting times_ are distributed as Poisson random variables.

The resulting distribution of points is similar to what you would get if you sampled the same number of points from a uniform distribution along the same interval. There's at least one important difference, however: if you choose points with waiting times according to the Poisson distribution, you don't know when starting out how many points are going to fit in the interval.
 
  • #4
benjaminmar8 said:
Let's say we deploy some random points on a line of finite length according to a poisson distribution of density \lambda.

That doesn't make sense, the Poisson distribution is unbounded, so not confined to a random line. Maybe you mean a random (and finite) set so that the number of points in any interval (a,b) is Poisson distributed with parameter [itex]\lambda(b-a)[/itex]? i.e., a http://books.google.co.uk/books?id=...bSNCw&sa=X&oi=book_result&ct=result&resnum=5".
Then, yes, you get the same thing as choosing N independent and uniform random variables, where N itself has the Poisson distribution.

(I don't see any paradox...)
 
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  • #5
To be more precise, for a Poisson process (where the interarrival times are exponential) given that there are N arrivals in [0,T] the arrival times T1,...,TN will be distributed according to the order statistics of N independent uniform variables.
 

Related to Are Poisson and Uniform Distributions Paradoxical on a Finite Line?

What is the Poisson and uniform paradox?

The Poisson and uniform paradox is a statistical phenomenon that occurs when two seemingly contradictory distributions, the Poisson distribution and the uniform distribution, are observed to produce similar results in certain situations. This paradox is a common topic of discussion in the field of statistics.

What is the Poisson distribution?

The Poisson distribution is a probability distribution that is used to model the number of events that occur in a fixed interval of time or space. It is often used to analyze data related to the occurrence of rare events, such as accidents or natural disasters.

What is the uniform distribution?

The uniform distribution is a probability distribution where all outcomes within a certain range are equally likely to occur. It is often used to model situations where there is no bias or preference towards any particular outcome.

How can two seemingly contradictory distributions produce similar results?

In the Poisson and uniform paradox, both the Poisson and uniform distributions can produce similar results when the sample size is small and there is a low probability of occurrence. This is because in these situations, the Poisson distribution approximates the uniform distribution.

What are some real-life examples of the Poisson and uniform paradox?

The Poisson and uniform paradox can be observed in various real-life scenarios, such as the number of car accidents that occur on a particular road in a given time period, or the number of customers that enter a store in a fixed amount of time. In both cases, the Poisson and uniform distributions can be used to model the data and may produce similar results.

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