Joint pdf/marginal/expectation related

  • Thread starter markov4
  • Start date
  • Tags
    Joint
In summary, the conversation discusses finding the joint probability density function (pdf) of X and Y, with the given information of a uniform probability distribution and the formula for the area of a triangle. The correct joint pdf is determined to be f_{X,Y}(x,y) = \frac{1}{A} within the triangle and zero outside, where A is the area of the triangle. This normalizes the joint pdf and ensures that the probability of picking any point in the triangle is uniform.
  • #1
markov4
9
0

Homework Statement


29v1637.jpg



Homework Equations


Well i know the double integral formula for the joint pdf


The Attempt at a Solution


So firstly we're told to find the joint pdf of X and Y. I'm not sure where to begin. So how do we find the function that we take the double integral over? I'm suspecting 1/2*b*h will be used and I know the joint pdf must equal 1, so do we equate this somehow to the formula for the area of a triangle? Could someone just point me in the right direction please? I suspect my bounds will be 0->1 and 0->x right? Or instead of the second one, 0->1-x ?

Thanks in advance!
 
Physics news on Phys.org
  • #2
so as there is uniform probabilty the area of any subset of the triangle will be directly proportional to the

say the area is A = (1/2).b.h

then the integral over the whole triangle
[tex] \int \int dxdy f_{X,Y}(x,y) = 1 [/tex]

The integrand is joint pdf [itex] f_{X,Y}(x,y) = f_{X,Y}(X=x,y=y) [/itex]

[itex] f_{X,Y}(x,y) dx dy [/itex] is the probability of finding X between x & x+dx and Y between y &y+dy

dxdy represents an area element. Knowing that and using the comments bove regrading the relation of probabilty and area, what can you say about [itex] f_{X,Y}(x,y) [/itex]?
 
  • #3
the bounds you have said look close, try setting up the whole double integral for the area of the triangle & i'll have a look at it, here's some latex code to help below (click on it)

[tex] A = \int_{a}^{b} dx \int_{f(x)}^{g(x)} dy [/tex]
 
Last edited:
  • #4
lanedance said:
dxdy represents an area element. Knowing that and using the comments bove regrading the relation of probabilty and area, what can you say about [itex] f_{X,Y}(x,y) [/itex]?
So knowing that the area of a triangle is (1/2)*b*h, and knowing that our joint pdf must integrate to one and it also incorporates the area under a triangle..then wouldn't [itex]f_{X,Y}(x,y)[/itex] be equal to (1/2)*b*h <--as in that's the function we take the double integral over, to get it to equal one? Where b=x and h=y (?).

That or we just set the LHS of the double integral formula equal to our equation for the area of the triangle, and find the integrals of dx and dy on the right side, then substitute through for b and h? Although i think the first way i described it makes more sense.
 
  • #5
not quite... if the area of the trangle is A = (1/2)*b*h, and you let the joint pdf be constant equal to [itex]f_{X,Y}(x,y) = A[/itex] within the triangle, zero outside

then if you integrate over the triangle
[tex]\int \int dx dy f_{X,Y}(x,y) = \int \int dx dy A = A \int \int dx dy = A(A) = A^2[/tex]
which is not equal to one


so you actually need [itex]f_{X,Y}(x,y) = \frac{1}{A}[/itex] within the triangle, and zero outside. This normalises the joint pdf correctly. The joint pdf is a constant function as the probabilty of picking any point in the triangle is uniform.
 
  • #6
I think i see now..thanks!
 

1. What is a joint probability density function (pdf)?

A joint probability density function (pdf) is a function that describes the probability of multiple random variables having specific values, when those variables are dependent on each other.

2. How is a joint pdf different from a marginal pdf?

A joint pdf describes the probability of multiple variables together, while a marginal pdf describes the probability of a single variable without considering the other variables.

3. What is the relationship between joint and marginal pdfs?

The marginal pdf can be obtained by integrating the joint pdf over all possible values of the other variables. This means that the joint pdf contains all the information needed to find the marginal pdfs of each individual variable.

4. What is the expected value of a random variable?

The expected value of a random variable is the average value that the variable takes on over a large number of trials. It is calculated by multiplying each possible value of the variable by its corresponding probability and summing these values.

5. How is the expected value related to the joint pdf?

The expected value of a function of multiple random variables can be calculated using the joint pdf. It is obtained by multiplying the function by the joint pdf and integrating over all possible values of the variables.

Similar threads

Replies
11
Views
1K
  • Calculus and Beyond Homework Help
Replies
6
Views
1K
  • Calculus and Beyond Homework Help
Replies
1
Views
2K
  • Calculus and Beyond Homework Help
Replies
6
Views
2K
  • Calculus and Beyond Homework Help
Replies
2
Views
738
  • Introductory Physics Homework Help
Replies
7
Views
505
  • Calculus and Beyond Homework Help
Replies
3
Views
2K
  • Calculus and Beyond Homework Help
Replies
3
Views
2K
  • Calculus and Beyond Homework Help
Replies
5
Views
1K
  • Calculus and Beyond Homework Help
Replies
6
Views
2K
Back
Top