What is the moment generating function from a density of a continuous

In summary, the conversation discusses a problem involving finding the mgf of a random variable with a given probability density function. The approach for solving this problem is to write down the integral and evaluate it. The other person in the conversation suggests showing what has been done so far to receive further help.
  • #1
karthik666
11
0
Hi everyone,

So I am taking a statistics course and finding this concept kinda challenging. wondering if someone can help me with the following problem!

Let X be a random variable with probability density function $$f(x)=\begin{cases}xe^{-x} \quad \text{if } x>0\\0 \quad \text{ } Otherwise.\end{cases} $$
we want to Determine the mgf of X whenever it exists.

I know that M(t) = E(e^(tx)) = integral of e^(tx)* f(x)
but not sure what to do from there.

Thanks for the help ^^
 
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  • #2
It's the integral of e^{tx}*f(x) over $\mathbb{R}$.
 
  • #3
what is the approach for this problem?
 
  • #4
You should write down explicitly the integral you said you're evaluating, and then try to evaluate it. Why don't you show us what you can do with it and then we can help you with where you get stuck? We can't help you until you do that.
 
Last edited:

Related to What is the moment generating function from a density of a continuous

What is a moment generating function?

A moment generating function is a mathematical function that generates moments (expected values) of a random variable. It is a useful tool in probability and statistics for analyzing the properties of a random variable.

How is a moment generating function related to a density function?

A moment generating function is related to a density function through the use of integrals. The moment generating function can be used to derive the moments of a random variable, which in turn can be used to calculate the moments of a density function.

What is the formula for calculating a moment generating function?

The formula for calculating a moment generating function is M(t) = E[etX], where X is a random variable and E is the expected value. This formula can be used for both discrete and continuous random variables.

What is the significance of the moment generating function?

The moment generating function has several important uses in probability and statistics. It can be used to calculate the moments of a random variable, which can then be used to calculate other statistical measures such as variance and skewness. It can also be used to prove the central limit theorem, which states that the sum of a large number of independent random variables will be approximately normally distributed.

How can the moment generating function be used in practical applications?

The moment generating function can be used to calculate various properties of a random variable, which can then be used in practical applications such as risk assessment, forecasting, and hypothesis testing. It is also used in the development of statistical models and in data analysis.

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