Limits of Integration for Joint Distribution problems

In summary: That is the density function of x for given y. Similarly, the "marginal density" of y for given x is\int_{y= 0}^{1- x^2}\frac{4}{3}dy= \frac{4}{3}(1- x^2) In summary, given a region R defined by 0≤ y ≤ 1-x2 and -1≤ x ≤ 1, the joint density function fxy(x,y) = 3/4. To find the marginal densities of X and Y, integrate over the respective variables while keeping the other variable fixed. For the marginal density
  • #1
trap101
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0
Suppose that (X,Y) is uniformly distributed over the regiondefined by 0≤ y ≤ 1-x2
and -1≤ x ≤ 1.

a) find the marginal densities of X and Y


Attempted solution:

So first I have to find the joint density function which ends up being fxy(x,y) = 3/4

and then from that I would solve for the marginal densities. Since there was a solution I was able to do these things, but my issue is finding the limits of integration.

in finding the joint density, why did they use -1 to 1 as the limit of integration and (1-x2) to find the joint density function. Then to find the marginal densities, they used 0 to 1-x2 to find the marginal density of X and ± (1-y)1/2 to find the marginal density of Y. How and why did these limits occur?
 
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  • #2
I assume you took a "multi-variable Calculus" course before this course. That is what is being used here. You are given that the region is "defined by 0≤ y ≤ 1-x2
and -1≤ x ≤ 1." To cover that region, x has to go from -1 to 1 and, for each x, y goes from 0 to [itex]1- x^2[/tex]. Since you are also given that this is a uniform probability distribution, a constant over the region, whicy we can call "M", and so the integral over the entire region must be
[tex]\int_{x=-1}^1\int_{y= 0}^{1- x^2} M dydx= 1[/tex]
Which gives M= 4/3. That's what you did isn't it?

Now, look at the "inner integral":
[tex]\frac{4}{3}\int_{y= 0}^{1- x^2} dy[/tex]
That is the integral with respect to y for fixed x. It is the probability of y for fixed x which is what is meant by "marginal probability".

Now, look at the region described by "[itex]-1\le x\le 1[/itex]" and "[itex]0\le y\le 1- x^2[/itex]. That is the region under the parabola [itex]y= 1- x^2[/itex] between x= -1 and x= 1. Clearly y can take on values as low as 0 or as high as 1. For each y, then x can go from the parabola on the left to the parabola on the right. Since the parabola is given by [itex]y= 1- x^2[/itex], [itex]x^2= 1- y[/itex] and [itex]x= \pm\sqrt{1- y}[/itex]. The left side is, of course, [itex]x= -\sqrt{1- y}[/itex] and the right side is [itex]x= +\sqrt{1- y}[/itex].

That is, if you were to integrate
[tex]\int_{y= 0}^1\int_{x=-\sqrt{1- y}}^{\sqrt{1- y}}\frac{4}{3}dxdy[/tex]
You would get "1" again. Removing the outside integral gives the "marginal probabilty" of x for a given y:
[tex]\int_{x= -\sqrt{1- y}}^{\sqrt{1- y}}\frac{4}{3}dx[/tex]
 

FAQ: Limits of Integration for Joint Distribution problems

What are the limits of integration for joint distribution problems?

The limits of integration for joint distribution problems depend on the type of problem and the variables involved. Typically, the limits will be the minimum and maximum values for each variable, within the given range or domain.

How do I determine the limits of integration for a joint distribution problem?

To determine the limits of integration, you will need to analyze the problem and identify the variables involved. Then, consider the range or domain for each variable and use these values as the limits of integration for that variable.

Are there any rules or guidelines for setting the limits of integration for joint distribution problems?

There are no specific rules or guidelines for setting the limits of integration for joint distribution problems. However, it is important to consider the range or domain of each variable and ensure that the limits are appropriate for the problem at hand.

Can I use different limits of integration for each variable in a joint distribution problem?

Yes, you can use different limits of integration for each variable in a joint distribution problem. This may be necessary depending on the nature of the problem and the relationships between the variables.

What happens if I use incorrect or inappropriate limits of integration for a joint distribution problem?

If you use incorrect or inappropriate limits of integration for a joint distribution problem, you may end up with an incorrect solution. It is important to carefully consider the problem and choose appropriate limits to ensure an accurate result.

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