Distributions with Infinite Mean: Examples & Possibilities

In summary, the conversation discusses the possibility of a probability distribution with an infinite mean. The participants suggest using examples and working backwards to construct such a distribution. An explicit example is given, but it is noted that the expectation value for this distribution is infinite and therefore requires infinite resources to implement. The conversation ends with a mention of the Cauchy random variable as an example of a distribution without a mean or variance.
  • #1
EvLer
458
0
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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  • #2
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.
 
  • #3
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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  • #4
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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  • #5
thanks for the help!
 
  • #6
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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What are distributions with infinite mean?

Distributions with infinite mean are probability distributions in which the mean (or expected value) is infinite. This means that the average value of the data or random variable is not well-defined and can be infinitely large.

What are some examples of distributions with infinite mean?

Some examples of distributions with infinite mean include the Cauchy distribution, the Pareto distribution, and the log-normal distribution with a shape parameter greater than 1. These distributions have heavy tails or extreme outliers that contribute to their infinite mean.

What is the significance of distributions with infinite mean?

Distributions with infinite mean have important implications in statistics and probability theory. They violate the assumptions of traditional statistical methods and require specialized techniques for analysis. They also represent situations in which extreme values or outliers occur more frequently than in normal distributions.

What are the possibilities of distributions with infinite mean?

The possibilities of distributions with infinite mean are vast and varied. They can arise in many fields of study, including finance, physics, and biology. They can also have practical applications, such as in modeling extreme events or outliers in data. Additionally, the study of distributions with infinite mean can lead to a better understanding of the behavior of extreme values in real-world situations.

How are distributions with infinite mean different from other distributions?

Distributions with infinite mean differ from other distributions in that they have a mean that is not well-defined. This leads to differences in their properties, such as the lack of a finite variance and the presence of heavy tails. They also require specialized techniques for analysis and have unique applications and implications.

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