Mgf of continuous random variables

In summary, the conversation was about finding the MGF of a continuous random variable with the PDF f(x) = 2x, 0<x<1. The speaker had initially expressed uncertainty about how to compute the integral for the MGF, but later realized that integrating by parts would give 2e^s(s-1+1)/s^2. They also discussed the MGF formula for a transformed random variable Y = aX + b and how to apply it.
  • #1
nacho-man
171
0
i have a simple enough question

Find the MGF of a continuous random variable with the PDF:

f(x) = 2x, 0<x<1

I understand MGF is calculated as:

$$M(S) = \int_{-\infty}^{+\infty} e^{Sx} f(x)dx$$

which would give me

$$\int_{-\infty}^{+\infty} e^{Sx} 2xdx$$
but how would i compute this integral?edit: scratch that. I think i am on the right track here, someone check?

if Y = aX + b, then
$$M_{y}(S) = E[e^{2S}] = e^{S}E[E^{2Sx}]$$

$$M_{y}(S) = e^S \int_{0}^{1}e^{2Sx}dx
= ... $$

$$= e^{S}(\frac{e^{2S}}{2s} - \frac{1}{2S}) $$
Is this correct?/ Am I on the correct track?

On another note, let's celebrate me getting the hang of latex! Yay (Clapping)
 
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  • #2
nacho said:
I understand MGF is calculated as:

$$M(S) = \int_{-\infty}^{+\infty} e^{Sx} f(x)dx$$

which would give me

$$\int_{-\infty}^{+\infty} e^{Sx} 2xdx$$
but how would i compute this integral?
Integrating by parts gives \[\int_{0}^{1} e^{Sx}2x\,dx =2\frac{e^s(s-1)+1}{s^2}\]You can check WolframAlpha.

nacho said:
if Y = aX + b, then
$$M_{y}(S) = E[e^{2S}] = e^{S}E[E^{2Sx}]$$
What? Where did $a$ and $b$ go? Also, $M_Y(S)=E[e^{YS}]$, not $E[e^{2S}]$.
 
  • #3
Edit: previous poster already replied
 

FAQ: Mgf of continuous random variables

What is the Mgf of a continuous random variable?

The Moment Generating Function (Mgf) of a continuous random variable is a mathematical function that is used to characterize the probability distribution of the variable. It is defined as the expected value of e^(tx) where t is a real number and x is the random variable.

How is the Mgf of a continuous random variable calculated?

The Mgf of a continuous random variable can be calculated by taking the integral of e^(tx) over the entire range of the variable. This integral can be solved using integration techniques, such as integration by parts or substitution.

What is the significance of the Mgf in probability theory?

The Mgf is an important tool in probability theory as it allows us to determine the moments of a continuous random variable, such as the mean, variance, and higher moments. It also helps in determining the probability distribution of the variable and can be used to derive other useful properties.

Can the Mgf be used to find the distribution of a continuous random variable?

Yes, the Mgf can be used to find the distribution of a continuous random variable. By taking the inverse Laplace transform of the Mgf, we can obtain the probability density function (PDF) of the variable. This allows us to fully characterize the probability distribution of the variable.

What are the limitations of using the Mgf of a continuous random variable?

The Mgf may not exist for all continuous random variables. In cases where the integral for the Mgf does not converge, the Mgf does not exist. Additionally, the Mgf may not always uniquely determine the probability distribution of a variable, especially in cases where the Mgf of two different distributions are the same.

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