What is the Moment Generating Function and How is it Used in Probability?

In summary, the book never really explains what the Moment Generating Function is, and the teacher wasn't very clear either. The homework statement is that if you toss a fair die and it comes up with an outcome x, then p(x) is 1/6. To simplify, you take the coefficient of the nth term of the power series and add it to the expectation value of the die. This equation becomes simpler if you express it in terms of r and t. If you know r and t, you can solve for the nth moment. The attempt at a solution starts with the expectations equation and uses the partial sum formula to get it in closed form. Lastly, the simplified equation is awkward compared to the original, and doesn
  • #1
exitwound
292
1
I haven't taken math for going on 13 years and I am having a hard time following what this all means. I'm not sure if I'm doing it right.

First of all, can anyone explain to me in English what exactly the Moment Generating Function is? The book does a poor job at it and the teacher wasn't very clear either. In fact, the book *never* says what it is at all, just how it's used.

Homework Statement



If you toss a fair die with outcome X, p(x) = 1/6 for x=1,2,3,4,5,6. Find Mx(t).

Homework Equations



[tex]M_x(t) = E(e^{tx}) = \sum_{x\in D}e^{tx}p(x) [/tex]

The Attempt at a Solution



If we start with the Expectation equation above:

[tex]\sum_{x\in D}e^{tx}p(x) = \sum_{x=1}^6 e^{tx}(1/6)[/tex]

[tex]= e^{t\cdot 0}(1/6) + e^{t\cdot 1}(1/6) + e^{t\cdot 2}(1/6) + ... e^{t\cdot 6}(1/6)[/tex]

[tex] = 1 + \frac{e^t}{6} + \frac{e^{2t}}{6} + \frac{e^{3t}}{6} + \frac{e^{4t}}{6} + \frac{e^{5t}}{6} + \frac{e^{6t}}{6}[/tex]

First of all, is this correct so far? And if it is, since it's been umteen years since I've had algebra, what happens here? How can this be simplified?
 
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  • #2
Why do you start your sum with

[tex]
e^{t\cdot 0}
[/tex]

when none of the sides have 0 dots? Other than that your initial setup is fine. As for simplifying,

*notice a factor common to the sum of 6 terms, and factor it out
*how would you simplify the geometric sum [tex] 1 + a +a^2 + a^3 + a^4 + a^5 [/tex]

I'm not sure the simplified form is much less awkward than the original form.
 
  • #3
The one in the beginning shouldn't be there because x=0 isn't a possible outcome of rolling a die.

The idea of a moment generating function is that if you write it out as a power series in t, the coefficient of [itex]t^n[/tex] is the nth moment divided by n!.

To simplify your expression, note that if [itex]r=e^t[/tex], your moment generating function is

[tex]M=(1/6)(r+r^2+r^3+r^4+r^5+r^6)[/tex]

Use the formula for the partial sum of a geometric series to get it in closed form.
 
  • #4
You're right about the 0 sides on a die. My mistake on that one.

As for "using the formula for the partial sum of a geometric series to get it in closed form", that's speaking MoonMan. I haven't had geometric series in over a decade. I'll have to do some digging. Ugh.

Also, I don't know what you mean by:
The idea of a moment generating function is that if you write it out as a power series in t, the coefficient of is the nth moment divided by n!.
To be completely honest, I don't even know what t is. I'm not a math major and the language is mostly foreign to me.
 
Last edited:
  • #5
If you expand the exponential as a Maclaurin series, you get

[tex]M_x(t) = E(e^{tx}) = E\left[1+tx+\frac{1}{2!}(tx)^2+\frac{1}{3!}(tx)^3+\hdots\right]=E(1)+E(x)t+E(x^2)(t^2/2!)+E(x^3)(t^3/3!)+\hdots[/tex]

So you can each term is proportional to a moment of x. If you have M(t), you can find the nth moment using the formula

[tex]E(x^n)=\left \frac{d^nM}{dt^n}\right|_{t=0}[/tex]

I don't know of an intuitive interpretation of what t is. I just view it as a bookkeeping device.
 

FAQ: What is the Moment Generating Function and How is it Used in Probability?

1. What is a moment generating function (MGF)?

A moment generating function is a mathematical tool used in probability and statistics to describe the distribution of a random variable. It is defined as the expected value of e^tx, where t is a real number and x is the random variable.

2. How is a moment generating function different from a probability generating function?

A moment generating function is a function of a continuous variable, while a probability generating function is a function of a discrete variable. Additionally, a moment generating function is defined for all real values of t, while a probability generating function is only defined for positive integer values of t.

3. What is the purpose of using a moment generating function?

Moment generating functions are useful because they provide a way to find the moments of a distribution. The nth moment of a distribution can be found by taking the nth derivative of the moment generating function and evaluating it at t=0. This allows for a simple and efficient way to calculate moments, which can be used to describe the shape, location, and dispersion of a distribution.

4. Are moment generating functions limited to certain types of distributions?

No, moment generating functions can be used for any type of distribution, as long as the expected value exists. However, they are most commonly used for normal, exponential, and gamma distributions.

5. Can moment generating functions be used to find the probability of an event?

No, moment generating functions are not directly used to find probabilities. However, they can be used to calculate the moments of a distribution, which can then be used to find probabilities using other statistical tools such as the central limit theorem.

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