Finding the mean of the gamma distribution

In summary: So what's the expected value of this gamma distribution?In summary, the mean of the gamma distribution can be found by reparameterizing r->r+1 and using the no-calculus method of finding the expected value of the new gamma distribution. This involves recognizing the fact that the integral of the original gamma distribution must equal 1, and using this information to solve for the mean.
  • #1
thomas49th
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Finding the mean of the gamma distribution ("reparameterise")

Homework Statement


Find the mean of the gamma distribution.
[tex]f_{X}(x) = \frac{e^{-kx}x^{r-1}k^{r}}{(r-1)!} x>0, r ε N*, k>0[/tex]


Homework Equations


Interestingly this is the alternative form of the gamma distribution


The Attempt at a Solution



I didn't really know where to begin, so peeked at the answers. Apparently I have to reparameterise r→r+1. Can someone explain to me what this means? How does it differ from a substitution? Is there a way to solve it without reparameterising and how do I spot when to reparameterise?

Thanks
Thomas
 
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  • #2
anybody?
 
  • #3


thomas49th said:

Homework Statement


Find the mean of the gamma distribution.
[tex]f_{X}(x) = \frac{e^{-kx}x^{r-1}k^{r}}{(r-1)!} x>0, r ε N*, k>0[/tex]
So$$
E(X) = \int_0^\infty xf_X(x)\,dx=\int_0^\infty \frac{e^{-kx}x^{r}k^{r}}{(r-1)!}\, dx$$Try a change of variable ##u=kx## and remember the definition of ##\Gamma(r)## and that ##\Gamma(r)=(r-1)!##.
 
  • #4


thomas49th said:

Homework Statement


Find the mean of the gamma distribution.
[tex]f_{X}(x) = \frac{e^{-kx}x^{r-1}k^{r}}{(r-1)!} x>0, r ε N*, k>0[/tex]


Homework Equations


Interestingly this is the alternative form of the gamma distribution


The Attempt at a Solution



I didn't really know where to begin, so peeked at the answers. Apparently I have to reparameterise r→r+1. Can someone explain to me what this means? How does it differ from a substitution? Is there a way to solve it without reparameterising and how do I spot when to reparameterise?

Thanks
Thomas
Here's a hint for an easy no-calculus solution. By definition, the mean is the integral of

[tex]x f_{X}(x) = \frac{e^{-kx}x^{r}k^{r}}{(r-1)!} = \frac{e^{-kx}x^{r}k^{r+1}}{r!} \cdot \frac{r}{k} [/tex]

Hmm... isn't that the pdf of a gamma distribution with parameters k and r+1, times r/k?

And doesn't a pdf integrate to 1?
 

FAQ: Finding the mean of the gamma distribution

What is the gamma distribution?

The gamma distribution is a continuous probability distribution that is commonly used to model waiting times or lifetime data. It is a flexible distribution that can take on a wide range of shapes depending on its parameters.

How is the mean of the gamma distribution calculated?

The mean of the gamma distribution is calculated by using the formula μ = αβ, where α is the shape parameter and β is the scale parameter. In simpler terms, the mean is equal to the shape parameter multiplied by the scale parameter.

What is the significance of the mean in the gamma distribution?

The mean of the gamma distribution is an important measure as it represents the average value of the data. It can be used to describe the central tendency of the data and is often used in hypothesis testing and parameter estimation.

How does changing the shape parameter affect the mean of the gamma distribution?

Changing the shape parameter will also change the mean of the gamma distribution. A larger shape parameter will result in a larger mean, while a smaller shape parameter will result in a smaller mean. This is because the shape parameter affects the overall shape of the distribution curve.

Can the mean of a gamma distribution be negative?

No, the mean of the gamma distribution cannot be negative. Since the gamma distribution is a probability distribution, the mean must fall within the range of possible values, which is always positive. A negative mean would not make sense in the context of the gamma distribution.

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