Probability: Discrete Random Variable

In summary, the problem involves finding the expected value, variance, and distribution for a discrete random variable X with a probability generating function of G(z) = z^2 * exp(4z-4). The moment generating function is not very convenient for this problem, so it's better to use the probability generating function. The first step would be to find the distribution using the formula G(z) = E(z^X) = ∑(k=0 to n) P(X=k) * z^k. Then, the definitions for expectation and variance can be used to find their values.
  • #1
stosw
21
0

Homework Statement


Suppose X is a discrete random variable whose probability generating function is
G(z) = z^2 * exp(4z-4)


Homework Equations


No idea


The Attempt at a Solution


I'm thinking that due to the exponent on the z term, that the exp(4z-4) would be the
P[X=3] = exp(4z-4), but I'm not even sure of this.

I honestly have no idea where to even start on a problem like this. Any sort of guidance would be great.
 
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  • #3
All the info given in [1] is what was given for the problem, I forgot to say that I am suppose to find the expected value, var[x], and the distribution on x.

I honestly have no idea what I am doing. Any hints would be great.
 
  • #4
Well, that link has the information. Have a look, have a go, and then show us where you get stuck.

Start with [tex]m_X(t) = \sum_{k=0}^n {P(X=k) e^{kt}}[/tex]... to get the distribution, then use the definitions for expectation and variance.
 
  • #5
Simon Bridge said:
Well, that link has the information. Have a look, have a go, and then show us where you get stuck.

Start with [tex]m_X(t) = \sum_{k=0}^n {P(X=k) e^{kt}}[/tex]... to get the distribution, then use the definitions for expectation and variance.

What you have written is the "moment-generating function", rather that the probability generating function. For a discrete random variable [itex]X \in \{0,1,2,\ldots \}[/itex] the probability generating function is
[tex] G(z) \equiv E z^X = \sum_{k=0}^{\infty} P(X=k) z^k.[/tex]
See, eg., http://en.wikipedia.org/wiki/Probability-generating_function .


RGV
 
  • #6
What you have written is the "moment-generating function"
Why yes I did, and it is indeed - please see post #2, and the link from that post, for the reasoning behind that :)
 
  • #7
Simon Bridge said:
Why yes I did, and it is indeed - please see post #2, and the link from that post, for the reasoning behind that :)

Of course one can use the moment-generating function for discrete, integer-valued random variables, but it is not very convenient; the moment-generating function (or Laplace transform) works better for continuous random variables. In the OP's example, the mgf would be
[tex] M_X(t) = G(e^t) = e^{2t - 4 + 4e^t},[/tex]
which is not particularly nice to work with.

RGV
 
  • #8
Well... either way OP has a place to start.
 

FAQ: Probability: Discrete Random Variable

What is a discrete random variable?

A discrete random variable is a type of random variable that can only take on a countable number of values. These values are typically whole numbers and can be listed out and counted. Examples of discrete random variables include the number of heads in 10 coin tosses or the number of red balls in a bag of 20 balls.

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

The main difference between a discrete random variable and a continuous random variable is that a discrete random variable can only take on a countable number of values while a continuous random variable can take on any value within a certain range. For example, a discrete random variable can only take on the values of 1, 2, 3, etc. while a continuous random variable can take on values like 1.5, 2.75, or any other decimal value within a given range.

What is the probability distribution of a discrete random variable?

The probability distribution of a discrete random variable is a function that assigns probabilities to each possible value that the variable can take on. This function is often represented as a table or graph and shows the likelihood of each value occurring. It is important to note that the sum of all the probabilities in a probability distribution must equal 1.

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 value by its probability and then summing all of these products together. This value represents the average value that would be obtained if the random variable were to be repeatedly measured an infinite number of times.

Can a discrete random variable have a normal distribution?

No, a discrete random variable cannot have a normal distribution. The normal distribution is a type of continuous probability distribution, meaning that it is used to model continuous random variables. A discrete random variable can only take on a countable number of values, making it impossible to have a normal distribution.

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