Moment Generating Function of normally distributed variable

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Discussion Overview

The discussion revolves around finding the moment generating function (MGF) for a normally distributed variable X ~ N(0,1) and for the square of that variable, X². Participants explore different methods for deriving the MGF, particularly in relation to the chi-square distribution.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant expresses uncertainty about how to find the MGF for X², questioning whether to use a transformation identity or directly derive it from the normal distribution.
  • Another participant suggests that if the pdf of the chi-square distribution is available, the MGF can be derived similarly to the normal case without needing to use identities.
  • A participant mentions they have not been provided with a pdf function and are tasked only with finding the MGF for both Xi and (Xi)², leading to the assumption that the MGF for a random variable with a chi distribution is (1 − 2t)⁻¹/².
  • Concerns are raised about potentially losing marks if the chi-square pdf must be derived from the normal pdf, although it is noted that the problem does not explicitly require this derivation.
  • There is a suggestion to use the chi-square pdf directly for the MGF calculation, indicating a preference for clarity in the approach.

Areas of Agreement / Disagreement

Participants express differing views on whether to derive the chi-square pdf from the normal distribution or to use it directly. There is no consensus on the best approach to take for finding the MGF of X².

Contextual Notes

Participants highlight the lack of a provided pdf function and the implications this has for solving the problem. The discussion includes uncertainty about the requirements for deriving the chi-square distribution from the normal distribution.

johnaphun
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Hi guys,

I need to find the moment generating function for X ~ N (0,1) and then also the MGF for X2 . I know how to do the first part but I'm unsure for X2.

do i use the identity that if Y = aX then

MY(t) = E(eY(t)) = E(e(t)aX)

or do i just square 2pi-1/2e x2/2 and then solve as normal for an MGF?
 
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johnaphun said:
Hi guys,

I need to find the moment generating function for X ~ N (0,1) and then also the MGF for X2 . I know how to do the first part but I'm unsure for X2.

do i use the identity that if Y = aX then

MY(t) = E(eY(t)) = E(e(t)aX)

or do i just square 2pi-1/2e x2/2 and then solve as normal for an MGF?

Hey johnaphun and welcome to the forums.

For the chi-square distribution, are you given the pdf function or do you have to derive it from the normal function?

If you can use the definition of the pdf, then you do exactly the same thing as you did for the normal. If you need to derive the pdf for the chi-square distribution then you need to use a transformation method.

You don't need to use any identity, just the pdf of the appropriate distribution as well as the MGF definition.
 
chiro said:
Hey johnaphun and welcome to the forums.

For the chi-square distribution, are you given the pdf function or do you have to derive it from the normal function?

If you can use the definition of the pdf, then you do exactly the same thing as you did for the normal. If you need to derive the pdf for the chi-square distribution then you need to use a transformation method.

You don't need to use any identity, just the pdf of the appropriate distribution as well as the MGF definition.

Hi thanks for the reply,

I've not been given any pdf function, I've only been told that Xi is standardised normally distributed and to thus find the MGF for Xi (which i can do) and (Xi)2.

I assume it's asking for me to find an MGF for a random variable Xi with chi distribution which is (1 − 2 t)−1/2, I'm just not sure how to go about proving that.
 
Last edited:
johnaphun said:
Hi thanks for the reply,

I've not been given any pdf function, I've only been told that Xi is standardised normally distributed and to thus find the MGF for Xi (which i can do) and (Xi)2.

I assume it's asking for me to find an MGF for a random variable Xi with chi distribution which is (1 − 2 t)−1/2, I'm just not sure how to go about proving that.

That seems a little odd because at the very least you should be given the normal pdf function.

The way I would do it (i.e. the second part) is to use the chi-square pdf and then use the mgf and do some algebra to get the final mgf function.

The thing though is that if you have to derive the chi-square pdf from the normal pdf, then you may lose marks. It doesn't explicitly say you have to do this though, so you are probably in all likelihood, safe to just use the chi-square pdf.

Based on this information, do you know how to go about solving this problem?
 

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