Distributions with Infinite Mean: Examples & Possibilities

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Discussion Overview

The discussion revolves around the possibility of having a probability distribution for a random variable (rv) with an infinite mean. Participants explore examples, methods of construction, and the implications of such distributions, touching on both theoretical and practical aspects.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant questions the existence of a distribution with an infinite mean, linking it to the convergence of integrals or summations.
  • Another participant confirms that it is possible to have such distributions, referencing a previous example from a probability class but unable to recall the specific density function.
  • A suggestion is made to construct a distribution by starting with a non-converging sum and adjusting it to meet the criteria of a probability distribution, although the initial attempt does not yield a valid distribution.
  • An explicit example is provided where a friend offers a payout of 2^k dollars with a specific probability distribution, which sums to 1 but results in an infinite expectation value.
  • A participant mentions the Cauchy random variable, noting that it does not have a mean or variance, but questions whether this is equivalent to having an "infinite" mean.

Areas of Agreement / Disagreement

Participants generally agree that distributions with infinite means are possible, but there is no consensus on specific examples or the implications of such distributions. The discussion includes competing views on the nature of mean and variance in certain distributions.

Contextual Notes

Some participants express uncertainty regarding the definitions and conditions under which a mean can be considered infinite, as well as the implications of distributions that do not converge.

EvLer
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Is it possible to have a distribution of a rv with infinite mean?
Techinically, mean is the expected value so... if the integral/summation does not converge?
Does anyone have a specific example of such a distribution?
Thanks!
 
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For the sake of giving you quick answer, yes, it's possible. We've seen an exemple of a continuous rv that did not have a converging mean in my prob class last semester but I lended my notes so I can't check what the density function was.
 
You could try constructing one yourself!

Try working backwards. Let's do the discrete case for simplicity.

Write down a sum you know doesn't converge, and assume that what you wrote is the sum that calculates expected value. Once you do that, it's easy to compute the weight of each individual event.

Of course, to be a probability distribution, the sum of the weights has to be 1. Try computing it. You probably won't get 1... but if you get a finite value, do you see an easy way to modify your distribution so that it's a probability distribution?


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Well, let's try an example. I know that the sum:

0 + 1 + 4 + 9 + 16 + ... = sum_{i = 0 .. infinity} i^2

doesn't converge. Well, if this sum is computing the expected value, then

sum_{i = 0 .. infinity} i^2 = sum_{i = 0 .. infinity} i * P(i)

so, P(i) = i for all events i. Now, to see what the total weight is:

sum_{i = 0 .. infinity} P(i) = +infinity

Oh well, this one doesn't work. We have to try something else.
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Here's an explicit example. A friend tells you he will give you 2^k dollars for some non-negative integer k, and that the method he will use to determine the amount is such that the probability P(2^k) that you will get 2^k dollars is 2^k/3^(k+1) for k=0,1,2,... The probabilities add up to 1:

[tex]\sum_{k=0}^{\infty}P(2^k)=\sum_{k=0}^{\infty}\frac{2^k}{3^{k+1}}=\frac{1}{3}\cdot\frac{1}{1-\frac{2}{3}}=1[/tex]

But the expectation value

[tex]\sum_{k=0}^{\infty}P(2^k)2^k[/tex]

is infinite, so your friend needs to be infinitely rich to pull this off.
 
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thanks for the help!
 
The Cauchy random variable (http://en.wikipedia.org/wiki/Cauchy_distribution" ) doesn't have a mean (or variance). I suppose that's different from having an "infinite" mean though.
 
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