Obtaining marginal PDFs from joint PDF

In summary: Similarly for Y.In summary, the joint pdf given is (8+xy^3)/64) for -1<x<1 and -2<y<2, and the marginal PDFs for X and Y are 1/2 and 1/4 respectively. This indicates that the random variables X and Y are dependent. The marginal PDFs represent the density of X and Y when the other variable is ignored, and in this case, they both have a uniform distribution.
  • #1
JamieL
4
0

Homework Statement


Hi all,
I'm looking at the joint pdf F(x,y) = (8+xy^3)/64) for -1<x<1 and -2<y<2
(A plot of it is here: https://www.wolframalpha.com/input/?i=(8+xy^3)/64+x+from+-1+to+1,+y+from+-2+to+2 ...sorry about the ugly url) and trying to find the marginal PDFs for X and Y.




Homework Equations



I know I want to integrate the joint function with respect to Y and X in order to to get the marginal pdfs for X and Y, respectively. However, I'm running into trouble when I try to set the bounds for these integrals!



The Attempt at a Solution


As far as I can tell, X and Y don't seem to depend on each other in this sense; i.e. for marginal(X) i would have Integral([JointPDF]dy), from -2 to 2, which comes out to 1/2.
(Similarly, integrating with respect to x from -1 to 1 yields 1/4).
When I integrate these from their respective bounds (x from -1 to 1, y from -2 to 2) both come out to 1, as a proper pdf should. However the fact that both are independent of x and y values makes me think something might be wrong...does anyone have any suggestions as to what I might be doing wrong?
Thanks so much!
Jamie
 
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  • #2
JamieL said:

Homework Statement


Hi all,
I'm looking at the joint pdf F(x,y) = (8+xy^3)/64) for -1<x<1 and -2<y<2
(A plot of it is here: https://www.wolframalpha.com/input/?i=(8+xy^3)/64+x+from+-1+to+1,+y+from+-2+to+2 ...sorry about the ugly url) and trying to find the marginal PDFs for X and Y.




Homework Equations



I know I want to integrate the joint function with respect to Y and X in order to to get the marginal pdfs for X and Y, respectively. However, I'm running into trouble when I try to set the bounds for these integrals!



The Attempt at a Solution


As far as I can tell, X and Y don't seem to depend on each other in this sense; i.e. for marginal(X) i would have Integral([JointPDF]dy), from -2 to 2, which comes out to 1/2.
(Similarly, integrating with respect to x from -1 to 1 yields 1/4).
When I integrate these from their respective bounds (x from -1 to 1, y from -2 to 2) both come out to 1, as a proper pdf should. However the fact that both are independent of x and y values makes me think something might be wrong...does anyone have any suggestions as to what I might be doing wrong?
Thanks so much!
Jamie

There is nothing wrong; those _are_ the marginal densities! The fact that
[tex] f_{XY}(x,y) \neq f_X(x) f_Y(y)[/tex] just means that the random variables X and Y are dependent.

BTW: we usually try to use lower case letters (such as f) for densities and upper case letters (such as F) for cumulative distribution functions.
 
  • #3
Woah - cool!
The more I look at it the more it makes sense, I guess I was just thrown off because I'd never seen an example with a single number before!
If you don't mind my asking, what exactly does this imply? While I understand how to find them, I think I'm slightly by what exactly the marginal PDFs represent?
Thanks again for your help - I really appreciate it!
 
  • #4
JamieL said:
Woah - cool!
The more I look at it the more it makes sense, I guess I was just thrown off because I'd never seen an example with a single number before!
If you don't mind my asking, what exactly does this imply? While I understand how to find them, I think I'm slightly by what exactly the marginal PDFs represent?
Thanks again for your help - I really appreciate it!

They represent what they always do in such situations: f_X(x) is the density of X when Y is ignored, so the fact that it is a constant means that when X is looked at in isolation it has a uniform distribution.
 

1. What is the main purpose of obtaining marginal PDFs from a joint PDF?

The main purpose of obtaining marginal PDFs from a joint PDF is to analyze the behavior of individual variables in a multivariate system. This allows for a better understanding of the relationship between the variables and their impact on the overall system.

2. How are marginal PDFs calculated from a joint PDF?

Marginal PDFs are calculated by integrating the joint PDF over all possible values of the variables except for the one being analyzed. This results in a PDF that represents the distribution of that specific variable in the system.

3. What information can be obtained from the marginal PDFs?

The marginal PDFs provide information about the individual variables, such as their mean, variance, and skewness. They also show the relationship between the variables, including any correlations or dependencies.

4. Can the marginal PDFs be used to make predictions?

Yes, the marginal PDFs can be used to make predictions about the behavior of the individual variables in the system. By analyzing their distributions, it is possible to make informed predictions about their future values.

5. Are there any limitations to obtaining marginal PDFs from a joint PDF?

One limitation is that the individual variables may not be independent, which can affect the accuracy of the predictions made using the marginal PDFs. Additionally, obtaining accurate marginal PDFs may be difficult if the joint PDF is complex and high-dimensional.

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