Finding the Moment Generating Function

In summary: Okay, that's what I was missing was summing them. Thanks!If we sum them we are just left with 2, correct? x+2-x?
  • #1
joypav
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0
I'm working this problem for my math stat class. Here is what I have for it.

First of all, is this the correct method for finding MGF? I thought it was but I don't understand the answers I am getting.

How do I determine my values for t? For both I have t not equal to 0 because t is in the denominator.

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  • #2
joypav said:
I'm working this problem for my math stat class. Here is what I have for it.

First of all, is this the correct method for finding MGF? I thought it was but I don't understand the answers I am getting.

How do I determine my values for t? For both I have t not equal to 0 because t is in the denominator.

Hey joypav! ;)

More accurately, we have:
$$M_X(t) = E(e^{tX}) = \int_0^2 f(x)e^{tx}dx$$
It seems you have split up the 2 contributions, but we really need their sum.

I haven't checked your calculations, but we will indeed end up with a $t$ in the denominator.
And if we take the limit for $t\to 0$, we should find that $M_X(0)=\lim\limits_{t\to 0} M_X(t) = 1$.
 
  • #3
I like Serena said:
Hey joypav! ;)

More accurately, we have:
$$M_X(t) = E(e^{tX}) = \int_0^2 f(x)e^{tx}dx$$
It seems you have split up the 2 contributions, but we really need their sum.

I haven't checked your calculations, but we will indeed end up with a $t$ in the denominator.
And if we take the limit for $t\to 0$, we should find that $M_X(0)=\lim\limits_{t\to 0} M_X(t) = 1$.

Okay, that's what I was missing was summing them. Thanks!
 
  • #4
If we sum them we are just left with 2, correct? x+2-x?

Then when I integrate I get 2(e^2t-1)/t. But when I apply L'hospitals I get 4. I must be making an error.
 
  • #5
joypav said:
If we sum them we are just left with 2, correct? x+2-x?

Then when I integrate I get 2(e^2t-1)/t. But when I apply L'hospitals I get 4. I must be making an error.

Without checking your calculations, I conclude that:
$$M_X(t) = \int_0^1 e^{tx}xdx + \int_1^2 e^{tx}(2-x)dx
= \left(\frac{e^t}{t} - \frac{e^{t}-1}{t^2}\right) + \left(-\frac{e^t}{t} + \frac{e^{2t}-e^{t}}{t^2}\right)
= \frac{e^{2t}-2e^t + 1}{t^2} = \left(\frac{e^t-1}{t}\right)^2
$$
And:
$$\lim_{t\to 0}\frac{e^t-1}{t} \overset{L'H\hat opital}{=} \lim_{t\to 0}\frac{e^t}{1} = 1
$$
(For the record, mister L'Hôpital was French, and I feel we owe him the respect to at least spell his name properly, although to be fair, when he actually lived, his name was spelled L'Hospital. ;))
 

FAQ: Finding the Moment Generating Function

What is a moment generating function (MGF)?

A moment generating function is a mathematical tool used in probability and statistics to describe the probability distribution of a random variable. It is a function that generates moments of a random variable, which are numerical measures of its shape, such as mean, variance, and higher moments.

How is a moment generating function related to a probability distribution?

The moment generating function of a random variable uniquely determines its probability distribution. This means that if two random variables have the same moment generating function, they have the same probability distribution, and vice versa.

How do you find the moment generating function of a probability distribution?

The moment generating function can be found by taking the expected value of e^tX, where X is the random variable and t is a real number. This can be done using integration or by using mathematical properties of the probability distribution.

What is the importance of the moment generating function in statistics?

The moment generating function allows for easier computation of moments of a random variable, which are important in describing the shape and characteristics of a probability distribution. It also allows for the derivation of other useful functions, such as the characteristic function and cumulant generating function.

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

No, the moment generating function can only be used for random variables that have finite moments. This means that the expected value of e^tX must exist for all values of t in a certain interval. If a random variable does not have finite moments, other tools such as the characteristic function or Laplace transform may be used instead.

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