Moment of a probability distribution

In summary, the conversation discusses the proper way to solve for the Moment of a given equation, which involves integrating by parts. The correct solution involves differentiating properly and getting rid of the (n+1) term. The conversation ends with a thank you to the expert for their help and patience.
  • #1
dbb04
5
0
when you calculate the Moment of the following equation

[tex]

p(x)=\left\{\begin{array}{cc}2Axe^{-Ax^2},&\mbox{ if }
x\geq 0\\0, & \mbox{ if } x<0\end{array}\right.
[/tex]

We get

[tex]
Mn =2A \int_0^\infty x^{n+1}e^{-Ax^2}
[/tex]

solving it by parts I am getting

[tex]
Mn=(n+1)\int_0^\infty x^{n-1}e^{-Ax^2}
[/tex]

but, apparently, the right solution is

[tex]
Mn=n\int_0^\infty x^{n-1}e^{-Ax^2}
[/tex]


What am I doing wrong? What is the proper way to solve it? Could you please do it step by step?

Thanks
 
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  • #2
It looks like you didn't differentate properly when you integrated by parts:

[tex]\frac {d x^n}{dx} = n x^{n-1}[/tex]
 
  • #3
sorry, still not following you.
If we integrate by parts we have

[tex]
Mn =2A \int_0^\infty x^{n+1}e^{-Ax^2}
[/tex]

[tex]
\int_0^\infty u\frac{dv}{dx} dx=uv-\int_0^\infty v\frac{du}{dx} dx
[/tex]

where

[tex]
u=x^{n+1} \ \ \ \ \ \frac{du}{dx}=(n+1)x^n
[/tex]

and

[tex]
\frac{dv}{dx}=e^{-Ax^2} \ \ \ \ \ v= -\frac{e^{-Ax^2}}{2xA}
[/tex]

so

[tex]
Mn=2A \ \{-x^{n+1} \ \frac{e^{-Ax^2}}{2xA} \ \ |_0^\infty +\int_0^\infty \frac{e^{-Ax^2}}{2xA}(n+1)x^n dx \ \}
[/tex]

[tex]
Mn=0 +(n+1)\int_0^\infty x^{n-1} \ e^{-Ax^2} dx
[/tex]

So, How you get rid of the (n+1) term.
Thanks
 
  • #4
This should get you where you want to go:

[tex]\int_0^{\infty}x^{n+1} e^{-Ax^2} dx = - \frac {1}{2A}
\int_0^{\infty}x^n \frac {d}{dx}e^{-Ax^2}dx[/tex]
 
  • #5
Thanks Tide,

appreciate your patience
 

1. What is the moment of a probability distribution?

The moment of a probability distribution is a statistical measure that describes the shape, location, and spread of a distribution. It is calculated by taking the weighted average of the data points, with the weights being the probabilities of each data point occurring.

2. How is the moment of a probability distribution different from its mean?

The moment of a probability distribution is a more comprehensive measure than its mean. While the mean only considers the first moment (the center of mass) of the distribution, the moment takes into account higher moments, providing a more complete description of the distribution's characteristics.

3. What are the advantages of using moments in probability distributions?

Moments allow for a more detailed understanding of a distribution, as they provide information about its shape, location, and spread. They also facilitate comparisons between different distributions, as they are standardized measures that are not affected by changes in scale or location.

4. How are moments used in real-world applications?

Moments are used in a variety of fields, including physics, engineering, economics, and finance. They are used to model and analyze data, make predictions, and test hypotheses. In finance, moments are particularly useful in risk management and portfolio optimization.

5. Can moments be used for any type of probability distribution?

Yes, moments can be used for any type of probability distribution, including discrete and continuous distributions. However, for continuous distributions, the moments are usually defined as integrals instead of sums.

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