On discrete random variables

In summary, a discrete random variable can be defined as a random variable whose cumulative distribution function increases only by jump discontinuities. This means that the cdf jumps to a higher value only at certain points, which are the possible values that the random variable can take. The number of these jumps can be finite or countably infinite. The set of locations of these jumps does not have to be topologically discrete, meaning that the jumps can be dense on the real line. This type of discrete random variable is different from the commonly considered cases where the set of possible values is a topologically discrete set. Some discrete random variables, such as those with a distribution over rational numbers, can be considered as continuous but not absolutely continuous random variables.
  • #1
Postante
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I have seen the following "extension" of discrete random variables definition, from:
pediaview.com/openpedia/Probability_distributions
(Abstract)
"... Equivalently to the above, a discrete random variable can be defined as a random variable whose cumulative distribution function (cdf) increases only by jump discontinuities—that is, its cdf increases only where it "jumps" to a higher value, and is constant between those jumps. The points where jumps occur are precisely the values which the random variable may take. The number of such jumps may be finite or countably infinite. The set of locations of such jumps need not be topologically discrete; for example, the cdf might jump at each rational number."
Do you agree with the statement that the cdf of a DRV jumps at each rational number?
 
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  • #2
I'm inclined to agree, i.e. I think that a random variable whose cdf jumps at all the rationals can be called discrete.

The rationale? Well, the concept of random variable doesn't have anything to do with the topology of the space of values of rational numbers. It would be silly to require that the concept of a random variable being discrete depend on the topology of the value space.

And if you don't consider the topology, there's nothing to distinguish two random variables taking values in ℤ and a random variable taking values in ℚ. As the former is called discrete, so should the latter be.
 
  • #3
The problem is as it follows:
"In cases more frequently considered, this set of possible values is a topologically discrete set in the sense that all its points are isolated points. But there are discrete random variables for which this countable set is dense on the real line (for example, a distribution over rational numbers)."
This means that the jumps are dense on the real line, like those of Cantor's function.
I believe that such a "discrete" rv actually is a continuous, but not "absolute continuous", random variable. It is a singular distribution (Lukacs: "Characteristic Functions", Griffin, 2nd Ed, 1970).
 

1. What is a discrete random variable?

A discrete random variable is a type of random variable that can only take on a finite or countably infinite set of values. This means that there is a specific list of possible outcomes for the variable, and each outcome has a certain probability of occurring.

2. How is a discrete random variable different from a continuous random variable?

A discrete random variable only has a finite or countably infinite set of possible outcomes, while a continuous random variable can take on any value within a given range. Additionally, the probability of any specific outcome for a discrete random variable is a single point, while for a continuous random variable, the probability is a range of values.

3. What is the probability mass function for a discrete random variable?

The probability mass function (PMF) for a discrete random variable is a function that assigns a probability to each possible outcome of the variable. It is often represented as a table, graph, or formula.

4. How is the expected value of a discrete random variable calculated?

The expected value of a discrete random variable is calculated by multiplying each possible outcome by its corresponding probability, and then summing these values together. This can also be represented mathematically as E(X) = ∑ x*P(x), where x is the possible outcome and P(x) is the probability of that outcome.

5. Can a discrete random variable have a normal distribution?

No, a discrete random variable cannot have a normal distribution. The normal distribution is a continuous probability distribution, meaning that it is used to describe variables that can take on any value within a given range. Discrete random variables, on the other hand, can only take on specific values and therefore cannot have a normal distribution.

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