Marginal distribution question.

In summary: So for the first case, the correct limits would be from x to 1. For the second case, the correct limits would be from 0 to x.In summary, the conversation discusses calculating marginal distributions and choosing the appropriate intervals of integration. The speaker suggests changing the order of integration and gives hints on how to find the interval limits. They also discuss the concept of constraints and how they affect the number of possibilities. The summary concludes with the correct intervals for integrating out y and the formula for calculating the distribution of y.
  • #1
Drao92
72
0
Hello,
i have the following conditional distribution
fXY(x,y)=2, of 0<x<1 and x<y<1
When i calculate the marginal distribution of y i don't know what interval of integration to choose.
I made the graph of x<y<1 and the points(x,y) for x<y are above x=y on that graph but i don't know how to write that interval.
fX is http://www.wolframalpha.com/input/?i=integral+from+x+to+1+from+2dy
In class we had somthing similar but with 0<y<sqrt(x) and we took y from 1-sqrt(x) to 1 but i didnt understand why and x was 0<x<1.
 
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  • #2
Hey Drao92.

If you have to integrate out the x variable, try changing the order of integration to dxdy instead of dydx and integrate out the x variable.

Can you show us what the results are when you change the order of integration [i.e. the limits are [a,b] for y and [c,d] for x?]
 
  • #3
Hi,
I don't know why you make me do this. I know how to solve a double integral.
I have problems with finding the interval of integration when 0<x<y<1.
 
  • #4
If you want to find the marginal you just integrate out the other variable. All I was doing was giving hints on how to do that.
 
  • #5
dxdy is 2(d-c)(b-a)
dydx is the same.
Does it have a connection with the resul of fX which is 2-2x?
 
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  • #6
What limits did you get for integration for the y limits as a function of x? (Was it 0 to 1 - x by any chance?)
 
  • #7
No, i took from x to 1
integral from x to 1 from 2dy=fX(x)
But i think it gives the same result as if we take from 0 to 1-x.
 
  • #8
In your limit you have x < y < 1 which means y > x. This means that the limits can not be from 0 to x since the y component is greater (in a standard x,y plot you are looking at the upper triangle not the lower triangle of the unit square).

Try re-calculating the limits again and see what you get.
 
  • #9
Sorry, i did edit later my post.
Also, i have a question on 0 to 1-x;
if we consider y=1-x and we choose x=0.8 it results y=0.2 and in this case y<x. This is what i can't understand in finding interval limits with these conditions.
 
  • #10
Don't over-think this: Basically the joint distribution says you have a limit on the domain and we have to follow that.

In the case that you had y = 1 - x, this would be a particularly "slice" where if you had a distribution for the random variable X (or Y), then you would get with certainty the value of Y (or X).

Another way to think of it is that if your domain is a triangle (which it is for your question), then this distribution represents a specific "line" that passes through the triangle which is a uni-variate distribution in the same way that a line which is part of a two-dimensional figure is one-dimensional (you only need one parameter to describe a line).

It will make your life easier if you think of constraints.

Every time you add a condition (y < x, y = 1 - x, etc) you are basically making the number of possibilities smaller. The more conditions you have the easier something is to deal with. Understanding what those conditions mean geometrically (for example I outlined the line analogy) will make your life a lot easier when doing mathematics.
 
  • #11
Thanks a lot for explanation.
Then i think the integration interval is 0 to y or 1-y to 1. if x<y<1 and 1>x>0
And if y<x<1 and 1>x>0 the integration interval is y to 1 or 0 to 1-y. Am i correct?
Im integrated out dx.
My formula for fY(y) is integral from fXY(x,y)dx. Is from course.
This > http://www.wolframalpha.com/input/?i=integral+from+0+to+y+from+2dx = distribution of y. I think on seminars when we took the limits 1-sqrt(x) to 1 for calculating the distribution of y we did somthing wrong... my teacher from seminar doesn't explain anything :|.
 
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  • #12
If you are integrating out y, then your limits have to be in terms of x not y.
 

FAQ: Marginal distribution question.

1. What is a marginal distribution?

A marginal distribution is a probability distribution that shows the probability of a single variable, regardless of other variables. It is obtained by summing or integrating over all other variables in a joint probability distribution.

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

A marginal distribution focuses on a single variable, while a joint distribution considers the relationship between multiple variables. A joint distribution provides the probability of different combinations of values for all variables, while a marginal distribution only shows the probability of a single variable.

3. How is a marginal distribution calculated?

A marginal distribution can be calculated by summing or integrating the joint distribution over all values of the other variables. For discrete variables, this would be a simple sum, while for continuous variables, it would require integration.

4. What is the purpose of using a marginal distribution?

Marginal distributions allow us to study individual variables without being influenced by other variables. They also help us to understand the relationship between variables in a joint distribution by providing information on their individual probabilities.

5. Can marginal distributions be used in hypothesis testing?

Yes, marginal distributions can be used in hypothesis testing. They can help us to determine if there is a significant difference in the distribution of a single variable between different groups or conditions. However, it is important to note that the interpretation of marginal distributions in hypothesis testing should consider the joint distribution as well.

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