Probability: Discrete Random Variable

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Homework Help Overview

The discussion revolves around a problem involving a discrete random variable X and its probability generating function given by G(z) = z^2 * exp(4z-4). Participants are exploring how to derive probabilities, expected values, and variances from this function.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • Some participants suggest starting with the moment generating function to find the probability of X=3. Others express uncertainty about how to approach the problem and seek guidance on deriving the expected value and variance.

Discussion Status

Participants are actively discussing the relationship between the probability generating function and the moment generating function. Some have provided links to additional resources and suggested starting points for the original poster to clarify their understanding.

Contextual Notes

There is mention of confusion regarding the definitions of the moment generating function and the probability generating function, indicating a need for clarification on these concepts. The original poster has also noted a lack of understanding of how to proceed with the problem.

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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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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.
 
Well, that link has the information. Have a look, have a go, and then show us where you get stuck.

Start with m_X(t) = \sum_{k=0}^n {P(X=k) e^{kt}}... to get the distribution, then use the definitions for expectation and variance.
 
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 m_X(t) = \sum_{k=0}^n {P(X=k) e^{kt}}... 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 X \in \{0,1,2,\ldots \} the probability generating function is
G(z) \equiv E z^X = \sum_{k=0}^{\infty} P(X=k) z^k.
See, eg., http://en.wikipedia.org/wiki/Probability-generating_function .


RGV
 
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 :)
 
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
M_X(t) = G(e^t) = e^{2t - 4 + 4e^t},
which is not particularly nice to work with.

RGV
 
Well... either way OP has a place to start.
 

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