Finding the Moment Generating Function for Uniform Distribution on (0,1)

In summary, the moment generating function for a uniform distribution over the interval (0,1) is 1+t/2!+t^2/3!+t^3/4!+..., and the moments around the origin can be found by taking the nth derivative of this function and evaluating it at t=0. This gives the formula E[X^n] = 1/(n+1).
  • #1
cse63146
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0

Homework Statement



Let X be uniformly distributed over the unit interval (0,1). Determine the moment generating function of X, and using this, determine all moments around the origin.

Homework Equations





The Attempt at a Solution



I know that the MGT is M(x) = E[ext]

I'm just not sure how to start this problem. Could someone give me a hint, or point me in the right direction?
 
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  • #2
That makes it the integral from 0 to 1 of exp(x*t)*1*dx, doesn't it?
 
  • #3
What does the "1" exp(x*t)*1*dx supposed to represent?
 
  • #4
If the distribution is uniform over [0,1], then the pdf is f(x)=1, isn't it? The moment generating function is integral f(x)*exp(x*t)*dx. The 1 is the f(x).
 
  • #5
Can't believe I didn't realize that. Thanks again.
 
  • #6
So I did the integral and I got [tex]\frac{1}{t}e^t - \frac{1}{t}[/tex]

so the nth derivative of e^t is [tex]\frac{e^t}{t!}[/tex] (from taylor series)

but not sure what to do about the 1/t.
 
  • #7
The series expansion of e^t is 1+t+t^2/2!+t^3/3!+... Which is quite unlike what you said. The 1/t part cancels.
 
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  • #8
so all moments around the origin is just e^t (evaluated at t = 0)?
 
  • #9
cse63146 said:
so all moments around the origin is just e^t (evaluated at t = 0)?

No! Not at all! I was suggesting you expand the infinite series for e^t and use some algebra to write it in a way that's easier to handle for derivatives. You can work directly with e^t/t-1/t as well, but you can't just put t=0 after you take the derivative since that would be undefined - you would have to take the limit as t->0.
 
  • #10
I see what you mean. I get tn - 1/n!
 
  • #11
cse63146 said:
I see what you mean. I get tn - 1/n!

Good! You mean the sum of that for n=1 to infinity, right? I.e. 1+t/2!+t^2/3!+..., right? Now it's easy to find the nth derivative of that, also right?
 
  • #12
the nth derivative would be [(1-n)^n]*(tn - 1/n!)
 
  • #13
cse63146 said:
the nth derivative would be [(1-n)^n]*(tn - 1/n!)

Why on Earth would it be that? Look, take 1+t/2!+t^2/3!+t^3/4!+... What's the first derivative evaluated at t=0? Now do the second. Try the third. Do you see a pattern? Those are the moments. They are also pretty easy to compute from the pdf without even using a generating function. You should probably do the zero moment as well.
 
  • #14
probably wrong, but it look's like tn/(n+1)! but if I substitute t = 0, then I get 0.
 
  • #15
Yes, it's wrong. Can you help me out here? m(t)=1+t/2!+t^2/3!+t^3/4!+... That is your moment generating function isn't it? It's a infinite SUM of terms like you have suggested before. Not just a naked t^n/(n+1)!. It's a summation. What's the first derivative at t=0? Please, help me. Exactly ONE of the terms in that series has a nonzero first derivative at t=0. NONE of the others do.
 
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  • #16
So I was looking through my notes and found this:

[tex]M(0) + \sum M^{(n)}(0)\frac{t^{n-1}}{n!} =1 + \sum^{\infty}_{n=2} E[X^n] \frac{t^{n-1}}{n!}[/tex]

where E[Xn] = 1/(n+1) since it's uniformly distributed on (0,1)
 
  • #17
Yes. E[X^n]=1/(n+1). Because i) the integral from 0 to 1 of x^n is 1/(n+1). And ii) you can also read that off from the generating faction. If you take the nth derivative and set t=0 the only term that contributes is t^n/(n+1)!. All of the others vanish. And differentiating that term n times gives 1/(n+1).
 
  • #18
So it's supposed to be:

[tex]M(0) + \sum M^{(n)}(0)\frac{t^{n-1}}{n!} =1 + \sum^{\infty}_{n=2} \frac{1}{n+1} \frac{t^{n-1}}{n!}[/tex]

Kinda confused about t^n/(n+1)!.
 
  • #19
Forget the n. Just deal with say the first 3 or 4 moments. Try and prove that the first few moments of your distribution are the same as what you would get from the generating function. Or even the first 1 or 2. Any of them. I'm on the verge of giving up here. Just do numbers, forget the symbols.
 
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  • #20
*smacks head on keyboard*

can't believe it took me so long to get it. Thanks for being so patient, and sorry for all the trouble.
 

1. What is a Moment Generating Function?

A Moment Generating Function (MGF) is a mathematical function used to describe the probability distribution of a random variable. It is a useful tool in statistics and probability theory for calculating moments and other properties of a distribution.

2. How is a Moment Generating Function calculated?

The Moment Generating Function for a random variable X is calculated as M(t) = E(etX), where E() represents the expected value operator. This involves taking the expectation of the random variable raised to the power of t.

3. What are the advantages of using a Moment Generating Function?

A Moment Generating Function allows us to easily calculate moments of a distribution, such as the mean, variance, and higher order moments. It also allows us to derive other properties of a distribution, such as the moment-generating function of a sum of independent random variables.

4. Can all random variables have a Moment Generating Function?

Not all random variables have a Moment Generating Function. In order for a random variable to have an MGF, it must exist for all values of t in a certain range. For example, if the integral M(t) = E(etX) diverges for any value of t, then the random variable does not have an MGF.

5. How is a Moment Generating Function related to other types of generating functions?

A Moment Generating Function is a specific type of generating function used in statistics and probability theory. Other types of generating functions include probability generating functions, characteristic functions, and cumulant generating functions. These functions each have different properties and uses, but they are all related to the distribution of a random variable.

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