Prove the mean of the weibull distribution

In summary, the conversation discusses finding the value of u in terms of a and b for a random variable x with a Weibull Distribution. This is done by expressing k in terms of a and b and using a substitution method to solve the integral. The final solution is u = a^(-1/b) * gamma(1 + 1/b).
  • #1
Chantry
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Homework Statement


This is the full question, but we're only asked to do b.

A random variable x has a Weibull Distribution if and only if its probability density is given by f(x) = {kx^(b-a) *exp(-ax^b) for x > 0 }
where a and b > 0.

a) Express k in terms of a and b.
b) Show that u = a^(-1/b) * gamma(1 + 1/b)

Homework Equations



All of the following could be of use.
Mx(t) = ∫exp(xt)f(x) dx
and the limit as t → 0 of M'x(t) = u = mean
u = Ex(x) = ∫xf(x)dx
gamma(a) = ∫x^(1-a)exp(-x)dx

Also the integral of f(x) = 1.

The Attempt at a Solution



I tried making a substitution for x, so that I could get the gamma function out of the integral, but that x^b really throws me.

I tried integrating by parts, but that just gives an even more complicated expression. I wanted to get rid of part of the equation by setting it to one, but no matter how you treat it you'll have a nasty exp(-ax^b) to deal with.

There's obviously some trick I'm supposed to use to figure it out.

Can anyone provide any hints? I've spent a good couple hours both trying to solve it and googling similar solutions.
 
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  • #2
Bumping back to first page.
 
  • #3
What substitution did you try? What is the obvious one to get rid of the power within the exponential?
 
  • #4
Not sure why, but it's not letting me edit the first post.

It should be kx^(b-1) *exp(-ax^b) not ∫kx^(b-a) *exp(-ax^b), but anyway, I took another crack at it and I solved it!

u = ax^b
du = abx^(b-1) dx

∫kx^(b-1) *exp(-ax^b) dx
= k/(ab)∫exp(-u) du
= k/ab[1] = 1, so k = ab.

∫kx^(b) *exp(-ax^b) dx
= k∫x^(b) *exp(-ax^b) dx
= k/(ab)∫(u/a)^(1/b) exp(-u)du
= a^(-1/b)k/(ab) ∫u^(1/b) exp(-u) du
= a^(-1/b-1)k/b Γ(1/b + 1)
sub k = ab in and you get
=a^(-1/b)Γ(1/b + 1)

Thanks a lot! I guess I just needed to know that substitution was the method to solve it for me to be able to do the math.
 
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  • #5
Good show.
 

FAQ: Prove the mean of the weibull distribution

1. What is the Weibull distribution?

The Weibull distribution is a probability distribution that is commonly used to model the time-to-failure of a system or component. It is often used in reliability engineering to analyze the failure rate of products over time.

2. How is the mean of the Weibull distribution calculated?

The mean of the Weibull distribution can be calculated using the formula μ = λ σ where μ is the mean, λ is the scale parameter, and σ is the shape parameter.

3. What is the significance of the mean in the Weibull distribution?

The mean of the Weibull distribution represents the average time-to-failure of a system or component. It is a measure of central tendency and can be used to make predictions about the reliability of a product.

4. How can the mean of the Weibull distribution be proven?

The mean of the Weibull distribution can be proven mathematically by integrating the probability density function of the distribution over its entire range. The resulting value should equal the mean calculated using the formula μ = λ σ.

5. What are the assumptions made when proving the mean of the Weibull distribution?

The main assumption is that the data follows a Weibull distribution. Additionally, it is assumed that the data is independent and identically distributed, and that there are no censoring or truncation effects.

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