Deriving the MGF for the Weibull Distribution

In summary, the conversation discusses the process of deriving the MGF (moment generating function) for the Weibull distribution. The integral for E(e^tx) is provided and it is noted that the answer can be found using the gamma function and a series representation for e^(t*λ). The individual is trying to use a change of variable, u = (x/λ)^k, to solve the problem but is having difficulties. They request guidance and mention that the provided URLs may have temporary issues.
  • #1
donald17
6
0
I'm attempting to derive the MGF for the Weibull distribution. I know that E([tex]e^{tx}[/tex]), which equals the integral shown here:

http://www.wolframalpha.com/input/?i=Integrate%5Be^%28t*x%29*%28k%2F%CE%BB%29*%28x%2F%CE%BB%29^%28k-1%29*e^-%28x%2F%CE%BB%29^k%2Cx%5D

where the parameters are k and λ.

The answer is found here:

http://www.wolframalpha.com/input/?i=Sum%5B%28t^n+%CE%BB^n%29%2Fn%21%2C+{n%2C+0%2C+Infinity}%5D*gamma%281%2B%281%2Fk%29%29

So I see that I need to get the gamma function and the series representation for e^(t*λ) to show up in order to get the right answer. I've been trying to use a change of variable such as u = (x/λ)^k, and I feel like I've been getting close, but can't exactly get it right. Can someone guide me along with this? Thank you.

*For some reason it keeps putting a space in the URL, so just take them out
 
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  • #2
Those links you gave might be temporary URLs. I didn't get the first one to work.
 
  • #3
In the first url try copy and pasting the whole thing, but taking out the space between the 2 and the F

Similarly, for the second url take out the space between the I and the nfinity. If this doesn't work let me know and I'll attempt to repost what I'm trying to show in another format.
 

1. What is the Weibull distribution?

The Weibull distribution is a probability distribution that is commonly used to model the lifetime of a product or system. It is a continuous distribution with two parameters, shape and scale, and is often used in reliability engineering and survival analysis.

2. What is the moment generating function (MGF)?

The moment generating function (MGF) is a mathematical function that is used to describe the probability distribution of a random variable. It is defined as the expected value of the exponential function raised to the power of the random variable. It provides a way to calculate moments of a probability distribution and is particularly useful for deriving the properties of a distribution.

3. How is the MGF for the Weibull distribution derived?

The MGF for the Weibull distribution can be derived by taking the expected value of the exponential function raised to the power of the random variable, and then simplifying the resulting expression using the properties of exponential and gamma functions. This process involves calculating the integral of the probability density function (PDF) of the Weibull distribution.

4. What are the properties of the MGF for the Weibull distribution?

The MGF for the Weibull distribution has several important properties, including:

  • It exists only for certain values of the shape and scale parameters.
  • It is defined only for positive values of the random variable.
  • It is a monotone increasing function of the random variable.
  • It can be used to calculate moments of the Weibull distribution, including its mean, variance, and higher order moments.

5. How is the MGF for the Weibull distribution used in practical applications?

The MGF for the Weibull distribution can be used in practical applications to calculate various properties of the distribution, such as the mean and variance, which are important in reliability analysis. It can also be used to derive the cumulative distribution function (CDF) and the probability density function (PDF) of the Weibull distribution, which are commonly used in statistical modeling and data analysis. Additionally, it can be used to compare the Weibull distribution to other probability distributions and determine the best fit for a given dataset.

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