Finding expected value from the moment generating function

In summary, the MGF moment generating function given is mx(t) = (e^t -1)/t and the formula for finding EX is E{X^n} = lim as t approaches 0 of the nth derivative of mx(t). By using the series expansion for e^t, which is the summation of (t)^k / k!, the answer for EX is 1/2.
  • #1
oyth94
33
0
Suppose I have the MGF moment generating function mx(t) = (e^t -1)/t
How can I find EX?
 
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  • #2
oyth94 said:
Suppose I have the MGF moment generating function mx(t) = (e^t -1)/t
How can I find EX?

If a r.v. X has m.g.f. $\displaystyle m_{X} (t) = E \{e^{t\ X}\}$, then... $\displaystyle E\{X^{n}\} = \lim_{t \rightarrow 0} \frac{d^{n} m_{X} (t)}{d t^{n}}\ (1)$

In Your case is...

$\displaystyle E\{X\} = \lim_{t \rightarrow 0} \frac{t\ e^{t} - e^{t} + 1}{t^{2}} = \frac{1}{2}\ (2)$ Kind regards $\chi$ $\sigma$
 
  • #3
chisigma said:
If a r.v. X has m.g.f. $\displaystyle m_{X} (t) = E \{e^{t\ X}\}$, then... $\displaystyle E\{X^{n}\} = \lim_{t \rightarrow 0} \frac{d^{n} m_{X} (t)}{d t^{n}}\ (1)$

In Your case is...

$\displaystyle E\{X\} = \lim_{t \rightarrow 0} \frac{t\ e^{t} - e^{t} + 1}{t^{2}} = \frac{1}{2}\ (2)$ Kind regards $\chi$ $\sigma$

Thank you for your help. For how I did it was I used the series expansion for e^t which is the summation of (t)^k / k!
so
\(\displaystyle [(t^k / t!) - 1 ] / t\) = [(1 + t/1 + t^2 / 2! + t^3 / 3! + ...) - 1] / t
then the 1s cancel out and I can factor out the t so it becomes
= 1 + t/2! + t^2 / 3! + ...
so when i use the moment generating function
mx^(k) (0) = EX
do i sub in t=0 and then the answer will be 1?
how do I go on from there? or is the answer e?
Confused!
How do you solve it using the series expansion?
 
  • #4
oyth94 said:
Thank you for your help. For how I did it was I used the series expansion for e^t which is the summation of (t)^k / k!
so
\(\displaystyle [(t^k / t!) - 1 ] / t\) = [(1 + t/1 + t^2 / 2! + t^3 / 3! + ...) - 1] / t
then the 1s cancel out and I can factor out the t so it becomes
= 1 + t/2! + t^2 / 3! + ...
so when i use the moment generating function
mx^(k) (0) = EX
do i sub in t=0 and then the answer will be 1?
how do I go on from there? or is the answer e?
Confused!
How do you solve it using the series expansion?

The result You have obtained...

$\displaystyle \frac{e^{t}-1}{t} = 1 + \frac{t}{2!} + \frac{t^{2}}{3!} + ...\ (1)$

... is absolutely correct... what is the matter?...

Kind regards

$\chi$ $\sigma$
 
  • #5
chisigma said:
The result You have obtained...

$\displaystyle \frac{e^{t}-1}{t} = 1 + \frac{t}{2!} + \frac{t^{2}}{3!} + ...\ (1)$

... is absolutely correct... what is the matter?...

Kind regards

$\chi$ $\sigma$

okay so i forgot to mention i have to take the derivative after i factor/cancel out the t right. so then it becomes
1 + t/2! + t^2 /3! + ...
derivative --> 1/2 + 2t/6 + ...
and then after i take the derivative i set t=0 because mx1(0) = EX
so from there i will get
EX = 1/2 as you said before.
 

FAQ: Finding expected value from the moment generating function

What is the moment generating function (MGF)?

The moment generating function is a mathematical function that provides a way to calculate the expected value (mean) and variance of a random variable. It gives us a way to represent the distribution of a random variable using its moments.

How do you find the expected value from the moment generating function?

To find the expected value from the moment generating function, we simply take the first derivative of the function at t=0. This will give us the mean of the distribution. Alternatively, we can also take the second derivative of the function at t=0 to find the variance.

Why is the moment generating function useful?

The moment generating function is useful because it allows us to easily find the expected value and variance of a random variable, which are important measures in probability and statistics. It also provides a way to compare different distributions and analyze their properties.

Can the moment generating function be used for all types of random variables?

Yes, the moment generating function can be used for all types of random variables, as long as the function exists. However, it may not always be the most convenient or efficient method for finding the expected value and variance.

Are there any limitations to using the moment generating function?

One limitation of the moment generating function is that it may not exist for all random variables, especially those with heavy tails or undefined moments. Additionally, it may not always be easy or feasible to find the moment generating function, particularly for complex distributions.

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