Obtaining marginal PDFs from joint PDF

Click For Summary

Homework Help Overview

The discussion revolves around finding the marginal probability density functions (PDFs) from a given joint PDF, specifically F(x,y) = (8 + xy^3)/64 for the ranges -1 < x < 1 and -2 < y < 2. Participants are exploring the integration process required to derive the marginal PDFs for X and Y.

Discussion Character

  • Mathematical reasoning, Assumption checking

Approaches and Questions Raised

  • Participants discuss the integration of the joint PDF with respect to Y and X to obtain the marginal PDFs, expressing confusion about the bounds for these integrals. There is a consideration of the independence of X and Y in the context of their marginal distributions.

Discussion Status

Some participants have provided insights regarding the nature of the marginal densities and the implications of their results, noting that the marginal PDFs indicate dependence between the random variables. The conversation is ongoing, with questions about the interpretation of marginal PDFs being raised.

Contextual Notes

There is mention of potential confusion regarding the representation of marginal PDFs and their implications, particularly in relation to uniform distributions and the interpretation of independence between variables.

JamieL
Messages
4
Reaction score
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
 
Physics news on Phys.org
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
f_{XY}(x,y) \neq f_X(x) f_Y(y) 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.
 
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!
 
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.
 

Similar threads

Replies
7
Views
1K
Replies
11
Views
3K
Replies
6
Views
2K
Replies
5
Views
2K
Replies
9
Views
3K
  • · Replies 6 ·
Replies
6
Views
2K
  • · Replies 4 ·
Replies
4
Views
1K
  • · Replies 5 ·
Replies
5
Views
3K
  • · Replies 3 ·
Replies
3
Views
3K
  • · Replies 4 ·
Replies
4
Views
4K