Moment generating function problem

In summary, the conversation discusses finding the moment generating function of Y, given the probability density function of X and a transformation. The last part of the problem involves using the density of X to compute a probability involving Y.
  • #1
wannabe92
9
0
From the pdf of X, f(x) = 1/8 e^-x/8, x > 0, find the mgf of Y=X/4 +1. What is then the value of P(2.3 < Y < 4.1)?

Homework Statement

Homework Equations


Moment generating function of exponential distribution

The Attempt at a Solution


I have the mgf of X, which is 1/8 / (1/8 - t). I have also worked out the mgf of Y, which is e^t (1/8 (1/8 - t/4)), I think. The last part of this problem I've yet to resolve. Please do help!
 
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  • #2
wannabe92 said:
From the pdf of X, f(x) = 1/8 e^-x/8, x > 0, find the mgf of Y=X/4 +1. What is then the value of P(2.3 < Y < 4.1)?

Homework Statement




Homework Equations


Moment generating function of exponential distribution


The Attempt at a Solution


I have the mgf of X, which is 1/8 / (1/8 - t). I have also worked out the mgf of Y, which is e^t (1/8 (1/8 - t/4)), I think. The last part of this problem I've yet to resolve. Please do help!

Please state exactly what definition of mgf you are using; the one that I use (standard, I think) gives a very different result from yours.

As to the second question: I don't think the mgf has any relevance here; you need to relate the interval probabilities of Y to those of X, and use the density of X to compute the result. In other words, {a <= Y <= b} is the same as {a1 <= X <= b1} for some a1 and b1 related to a and b, and you know how to calculate P{a1 <= X <= b1}.

RGV
 
  • #3
I'm using the mgf of exponential: lambda/(lambda -t). So, to obtain the probability, simply integrate the probability density function of X with the values of a1 and b1, is that it?
 

Related to Moment generating function problem

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

A moment generating function is a mathematical function used in probability theory to describe the properties 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. What is the purpose of using a moment generating function?

The moment generating function allows us to calculate the moments (i.e. mean, variance, skewness, etc.) of a probability distribution. It also helps in simplifying calculations involving multiple random variables.

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

A moment generating function uniquely determines the probability distribution of a random variable. This means that if two random variables have the same moment generating function, they have the same probability distribution.

4. What are the advantages of using a moment generating function?

Using a moment generating function can simplify calculations involving multiple random variables, as well as allow us to easily find the moments of a probability distribution. It also has applications in statistics and hypothesis testing.

5. Are there any limitations to using a moment generating function?

One limitation of using a moment generating function is that it may not exist for all probability distributions, particularly those with infinite support. Additionally, the moment generating function may not be defined for certain values of t, making it difficult to use in calculations.

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