Finding Moments of $X\sim N(0,1)$ using MGF

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In summary: The MGF is found to be equal to $e^{s^2/2}$ and the moments are calculated using derivatives of the MGF. It is noted that the moments for odd powers of $X$ are all 0 while the moments for even powers follow a pattern. The person asks if it is enough to write the general formula for the $k$-th moment and how to prove it. In summary, the conversation discusses finding the MGF and moments of a normal distribution and how to calculate the moments using derivatives of the MGF.
  • #1
Jason4
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I have to find the moment generating function and find all the moments of $X\sim N(0,1)$

For the MGF, I have:

$M_X(s)=\displaystyle\int_{-\infty}^{\infty}e^s\frac{e^{x^2/2}}{\sqrt{2\pi}}\,dx = \ldots=e^{s^2/2}$

Next I found that:

$M'_X(0)=E[X]=0$

$M''_X(0)=E[X^2]=1$

$E[X^3]=0$

$E[X^4]=3$

$\ldots$

$E[X^{ODD}]=\{0\}$

$E[X^{EVEN}]=\{1,3,15,105,945,\ldots\}$

Is it enough to write:

$E[X^k]=M_X^{(k)}(0)=\frac{d^k}{ds^k}e^{s^2/2}$

Am I totally off track here? How would I prove this?
 
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  • #2
Jason said:
I have to find the moment generating function and find all the moments of $X\sim N(0,1)$

For the MGF, I have:

$M_X(s)=\displaystyle\int_{-\infty}^{\infty}e^s\frac{e^{x^2/2}}{\sqrt{2\pi}}\,dx = \ldots=e^{s^2/2}$

Next I found that:

$M'_X(0)=E[X]=0$

$M''_X(0)=E[X^2]=1$

$E[X^3]=0$

$E[X^4]=3$

$\ldots$

$E[X^{ODD}]=\{0\}$

$E[X^{EVEN}]=\{1,3,15,105,945,\ldots\}$

Is it enough to write:

$E[X^k]=M_X^{(k)}(0)=\frac{d^k}{ds^k}e^{s^2/2}$

Am I totally off track here? How would I prove this?

Th \(k\)-th moment is \(k!\) times coefficient of \(s^k\) in the MacLauren series expansion of the MGF.

CB
 

Related to Finding Moments of $X\sim N(0,1)$ using MGF

1. How is the Moment Generating Function (MGF) used to find moments of a normal distribution?

The MGF of a random variable is defined as the expected value of e^(tX), where t is a real number and X is the random variable. By taking derivatives of the MGF and evaluating them at t=0, we can find the moments of the distribution. For a normal distribution, the moments can be found by using the properties of the MGF for a normal distribution, which is e^(t^2/2).

2. What is the purpose of finding moments of a normal distribution using MGF?

Finding moments of a normal distribution using MGF allows us to calculate important characteristics of the distribution, such as the mean, variance, and higher moments. These moments provide information about the shape and behavior of the distribution, and can be used to make predictions and in statistical analysis.

3. Can the MGF be used to find moments of any type of distribution?

Yes, the MGF can be used to find moments of any distribution as long as the MGF exists for that distribution. However, for some distributions, the MGF may not exist or may be difficult to calculate, in which case other methods may be used to find the moments.

4. What are the main advantages of using the MGF to find moments of a normal distribution?

The main advantage of using the MGF is that it provides a systematic and efficient way to find moments of a distribution. It also allows for easy calculation of higher moments and can be used to find moments of complicated distributions by using the properties of the MGF.

5. Are there any limitations to using the MGF to find moments of a normal distribution?

One limitation of using the MGF is that it may not exist for some distributions, making it impossible to use this method to find moments. Additionally, the MGF may be difficult to calculate for some distributions, requiring advanced mathematical techniques. In these cases, other methods may be used to find moments of the distribution.

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