Moment generating function

In summary, a moment generating function (MGF) is a mathematical function used to uniquely describe a probability distribution. It is defined as the expected value of e^(tX), where t is a real number and X is a random variable. MGFs are commonly used in statistics to determine the moments of a distribution and to prove theorems. The relationship between MGFs and characteristic functions is that they both describe probability distributions, but MGFs are defined for all random variables while characteristic functions are only defined for continuous random variables. However, there are some probability distributions for which an MGF cannot be calculated, such as those with infinite moments or those without a finite expected value. MGFs can also be used in hypothesis testing
  • #1
boneill3
127
0

Homework Statement



Find the mgf of 2/25*(5-y) fo 0<y<5

Homework Equations



M(t) = INT e^yt f(y)dy

The Attempt at a Solution



= (2*(e^(5t)-5t-1))/25t

Is this ok
 
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  • #2
I got a different denominator. One of us is wrong, probably me?
 
  • #3
Sorry my mistake I forgot to square the denomiator Is this better?

= (2*(e^(5t)-5t-1))/(25t)^2
regards
Brendan
 
  • #4
25 shouldn't be squared.
 
  • #5
Oops
= (2*(e^(5t)-5t-1))/25t^2
Brendan
 

1. What is a moment generating function?

A moment generating function (MGF) is a mathematical function that provides a way to uniquely characterize a probability distribution. It is defined as the expected value of e^(tX), where t is a real number and X is a random variable.

2. How is a moment generating function used in statistics?

MGFs are used to determine the moments (mean, variance, skewness, etc.) of a probability distribution. They can also be used to find the joint moments of multiple random variables and to prove theorems in statistics.

3. What is the relationship between a moment generating function and a characteristic function?

The moment generating function and characteristic function are both mathematical functions used to describe probability distributions. The main difference is that the MGF is defined for all random variables, while the characteristic function is defined only for continuous random variables.

4. Can a moment generating function always be calculated for a given probability distribution?

No, there are some probability distributions for which an MGF cannot be calculated. This includes distributions with infinite moments or those that do not have a finite expected value.

5. How can moment generating functions be used in hypothesis testing?

MGFs can be used to test hypotheses about the parameters of a probability distribution. By comparing the MGF of a given sample to the MGF of a hypothesized distribution, we can determine the likelihood of the hypothesis being true.

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