Marginal distribution, double integral clarification

In summary, the problem involves finding the marginal probability functions f_{x}(x) and f_{y}(y) for a uniformly distributed area T = {(x,y): 0 < x < 2, -x < 2y < 0}. The boundaries for each function are rewritten and integrated accordingly, resulting in f(y) = 2 + 2y and f(x) = (1/2)x. To find E(X) and E(Y), integrate x*f(x) and y*f(y) over the full range of x and y, respectively.
  • #1
Gauss M.D.
153
1

Homework Statement



(X,Y) is uniformly distributed over the area

T = {(x,y): 0 < x < 2, -x < 2y < 0}

Find the marginal probability functions ie [itex]f_{x}(x)[/itex] and [itex]f_{y}(y)[/itex].

The Attempt at a Solution



The thing I'm having trouble with is that y depends on x. Am I supposed to rewrite the boundaries for each marginal function? It feels like I'm doing things a roundabout way!

F(x,y) = [itex]\int\int dx dy[/itex]

I.e. to find f(y):

-x < 2y < 0 [itex]\Leftrightarrow[/itex] x > -2y > 0 [itex]\Rightarrow[/itex] -2y < x < 2

Which means I can integrate with respect to x from -2y to 2, leaving me with f(y) = 2 + 2y

And if I instead want to find f(x):

-x < 2y < 0 [itex]\Leftrightarrow[/itex] -(1/2)x < y < 0

Which means I integrate with respect to y from -(1/2)x to 0, leaving me with f(x) = (1/2)x.

Again, it feels pretty roundabout and I wanted to make sure I wasn't missing anything.
 
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  • #2
Also, if I wanted to find E(X) and E(Y) here after finding f(x) and f(y), what interval should I integrate x*f(x) over, given that they are bounded by each other?
 
  • #3
Your method of extracting f(x) and f(y) looks fine, and gets the right answers.
For E(X), just integrate xf(x) over the full range of x, etc.
 
  • #4
Thanks Haruspex!
 

1. What is a marginal distribution?

A marginal distribution is a probability distribution of a subset of variables from a larger set of variables. It is obtained by summing or integrating over the other variables in the set. Marginal distributions are useful for understanding the relationship between variables in a dataset.

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

A joint distribution describes the probability of two or more variables occurring together, while a marginal distribution describes the probability of a single variable occurring. In other words, a joint distribution shows the relationship between variables, while a marginal distribution focuses on a specific variable.

3. What is a double integral?

A double integral is an integral with two variables, used to calculate the volume under a curved surface in two-dimensional space. It involves multiplying the function being integrated by an infinitesimal area element and integrating over the region of interest.

4. How is a double integral used in probability distributions?

In probability, a double integral is used to calculate the probability of an event occurring within a specific region of a joint probability density function. This is useful for understanding how two variables affect the likelihood of a particular outcome.

5. Can you provide an example of using a double integral to find a marginal distribution?

Yes, for example, if we have a joint probability density function f(x,y) and we want to find the marginal distribution of x, we can use the double integral ∫-∞ f(x,y) dy. This will sum or integrate over all possible values of y, leaving only the variable x in the resulting marginal distribution.

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