Finding the Moment Generating Function

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SUMMARY

The discussion focuses on finding the Moment Generating Function (MGF) for a given probability distribution. The correct method involves calculating the expected value using the formula $$M_X(t) = E(e^{tX}) = \int_0^2 f(x)e^{tx}dx$$. Participants clarify that summing contributions is essential, leading to the integration result $$M_X(t) = \frac{e^{2t}-2e^t + 1}{t^2}$$. The limit as \(t\) approaches 0 confirms that \(M_X(0) = 1\), validating the calculations.

PREREQUISITES
  • Understanding of Moment Generating Functions (MGF)
  • Familiarity with integration techniques in calculus
  • Knowledge of L'Hôpital's Rule for limit evaluation
  • Basic concepts of probability distributions
NEXT STEPS
  • Study the properties of Moment Generating Functions in probability theory
  • Practice integration techniques involving exponential functions
  • Explore L'Hôpital's Rule in depth for limit problems
  • Review the derivation of expected values for different probability distributions
USEFUL FOR

Students in mathematics or statistics, particularly those studying probability theory and moment generating functions, as well as educators looking for clarification on MGF calculations.

joypav
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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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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 $M_X(0)=\lim\limits_{t\to 0} M_X(t) = 1$.
 
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!
 
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.
 
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. ;))
 

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