Are Poisson and Uniform Distributions Paradoxical on a Finite Line?

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Discussion Overview

The discussion revolves around the relationship between Poisson and uniform distributions when deploying random points on a finite line. Participants explore whether points distributed according to a Poisson distribution can also be considered uniformly distributed, addressing theoretical implications and potential paradoxes.

Discussion Character

  • Debate/contested
  • Technical explanation
  • Conceptual clarification

Main Points Raised

  • Some participants assert that points from a Poisson distribution cannot be considered uniformly distributed, highlighting differences in how points are likely to cluster around certain values.
  • One participant suggests that the Poisson distribution describes the waiting time until the next event, implying a misunderstanding of the original question regarding the distribution of points.
  • Another participant clarifies that the Poisson distribution is unbounded and proposes that the question may refer to a finite set of points where the number of points in any interval follows a Poisson distribution.
  • It is noted that if points are chosen with waiting times according to the Poisson distribution, the resulting distribution of points resembles that of sampling from a uniform distribution, but with the caveat that the number of points is not predetermined.
  • A later reply discusses that for a Poisson process, the arrival times can be represented as order statistics of independent uniform variables, adding a layer of complexity to the relationship between the two distributions.

Areas of Agreement / Disagreement

Participants generally disagree on whether points from a Poisson distribution can be considered uniformly distributed. Multiple competing views remain regarding the interpretation of the distributions and their implications.

Contextual Notes

There are limitations in the discussion regarding the assumptions made about the nature of the distributions and the context in which they are applied. The relationship between the number of points and their distribution is not fully resolved, and the implications of using a Poisson process versus a uniform distribution are still under debate.

benjaminmar8
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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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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.
 
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.
 
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...)
 
Last edited by a moderator:
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.
 

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