Pdf and pmf as random variables?

In summary: In that case, the pmf is defined on the range of the random variable, which can be any set, not necessarily countable. Therefore, the pmf is not necessarily measurable. However, if the range is countable (which is often the case), then the pmf is measurable. In summary, a pdf is always measurable and a pmf is measurable if its range is countable, but not necessarily measurable if its range is uncountable.
  • #1
Rasalhague
1,387
2
If the set of real numbers is considered as a sample space with the Borel sigma algebra for its events, and also as an observation space with the same sigma algebra, is a pdf or pmf a kind of random variable? That is, are they measurable functions?
 
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  • #2
Hi Rasalhague! :smile:

Rasalhague said:
If the set of real numbers is considered as a sample space with the Borel sigma algebra for its events, and also as an observation space with the same sigma algebra, is a pdf or pmf a kind of random variable? That is, are they measurable functions?

Yes, a pdf is always measurable, it is even integrable. In fact, a pdf is defined to be integrable.
A pmf is certainly measurable since its domain is countable.

Almost all functions you will ever encounter in probability theory will be measurable, so this is (luckily) no exception to that rule.
 
  • #3
Hello, again, micromass!

micromass said:
A pmf is certainly measurable since its domain is countable.

Is a pmf also measurable when, as here, it's defined with an uncountable domain, namely the real numbers?
 
  • #4
Rasalhague said:
Is a pmf also measurable when, as here, it's defined with an uncountable domain, namely the real numbers?

Are you sure about that?
 
  • #5
Rasalhague said:
Hello, again, micromass!



Is a pmf also measurable when, as here, it's defined with an uncountable domain, namely the real numbers?

Yes, because there are at most countable non-boring numbers. That is, most of the numbers in the uncountable domain are being sent to 0, while only a countable number of them are interesting. This means that it's measurable.
 
  • #6
Aha, I think I see why it has to be! A pmf, fX, has (finitely or infinitely) countable range (because it has only countably many nonzero, i.e. non-boring elements), so every subset is a countable union of singletons, which are elements of the Borel algebra on R being complements of pairs of open sets. The pre-image of every subset not containing zero is a subset of the range of X, also a countable union of singletons, because X is discrete. The pre-image of every subset containing zero is a countable union of singletons and the open intervals between them, together with the open interval before the first element of the range of X and the open interval after the last. So these pre-images are also elements of the Borel algebra. So fX is measurable.
 
  • #7
disregardthat said:
Are you sure about that?

Sure that the Wikipedia article I linked to defines the pmf on R? Yes, unless it's been changed recently, it's in the 2nd sentence of "Formal definition" and reiterated in the sentence immediately after that.

Here's another source which defines the pmf on R.
 
  • #8
Rasalhague said:
Sure that the Wikipedia article I linked to defines the pmf on R? Yes, unless it's been changed recently, it's in the 2nd sentence of "Formal definition" and reiterated in the sentence immediately after that.

Here's another source which defines the pmf on R.

You are right, I accidentally thought you were talking about the random variable.
 

1. What is the difference between a Pdf and a Pmf as random variables?

A Pdf (Probability Density Function) is a function that describes the probability of a continuous random variable taking on a specific value. A Pmf (Probability Mass Function) is a function that describes the probability of a discrete random variable taking on a specific value. In other words, a Pdf is used for continuous random variables, while a Pmf is used for discrete random variables.

2. How do you calculate the mean of a Pdf or Pmf as a random variable?

The mean of a Pdf as a random variable is calculated by taking the integral of the function over its entire range, while the mean of a Pmf as a random variable is calculated by taking the sum of each value multiplied by its corresponding probability.

3. What is the relationship between a Pdf and a Cdf (Cumulative Distribution Function)?

A Pdf and a Cdf are related through integration. The Cdf is the integral of the Pdf, and it gives the probability that a random variable will be less than or equal to a certain value.

4. Can a Pdf or Pmf have negative values?

No, a Pdf and a Pmf cannot have negative values. They represent probabilities, which by definition cannot be negative.

5. How are Pdfs and Pmfs used in statistical analysis?

Pdfs and Pmfs are used in statistical analysis to describe the probability distribution of a random variable. They can be used to calculate probabilities, find the mean and variance of a random variable, and make predictions about future outcomes.

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