Conditional Distribution Functions

In summary, the joint distribution of (X1, X2) is 1/X1 on the region 0 < X2 < X1 < 1. The marginal distribution of X2 is X2~[0,X1] and the explicit density function for X2 is undefined due to the ln(0) term.
  • #1
Brandon1994
9
0

Homework Statement


If X1 is uniform on [0,1], and, conditional on X1, X2, is uniform on [0,X1], find the joint and marginal distributions of X1 and X2


Homework Equations



conditional joint distribution

The Attempt at a Solution



f(x1|x2) = 1/x1 (for 0<x2<x1)
f(x1) = 1 ( for 0<x1<1)

then
F(x1,x2) = Integrate (1/x1) from {x2,0,x2}{x1,0,x1}
I get an ln(0) when i try to integrate however

for the marginal distribution of x2, i get X2~[0,X1] //i am not sure if that's the answer they are looking for, if i try to write an explicit density function for X2 i get that the density is infinity, again due to the ln (0) term.

Thanks
 
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  • #2
Brandon1994 said:

Homework Statement


If X1 is uniform on [0,1], and, conditional on X1, X2, is uniform on [0,X1], find the joint and marginal distributions of X1 and X2


Homework Equations



conditional joint distribution

The Attempt at a Solution



f(x1|x2) = 1/x1 (for 0<x2<x1)
f(x1) = 1 ( for 0<x1<1)

then
F(x1,x2) = Integrate (1/x1) from {x2,0,x2}{x1,0,x1}
I get an ln(0) when i try to integrate however

for the marginal distribution of x2, i get X2~[0,X1] //i am not sure if that's the answer they are looking for, if i try to write an explicit density function for X2 i get that the density is infinity, again due to the ln (0) term.

Thanks

You have it exactly backwards: you are given ##f(x_2|x_1)##, not ##f(x_1|x_2).##

Also: what does the notation "Integrate(1/x1) from {x2,0,x2}{x1,0,x1}" mean? I have never seen that before.

Anyway, the first thing to do is to answer the question "what is the joint distribution of ##(X_1,X_2)?## You have not done that.
 
  • #3
wouldn't the joint density be:
f(X1,X2) = 1/X1

and I was saying to find the marginal density f(X2) you would integrate the above expression
 
  • #4
Brandon1994 said:
wouldn't the joint density be:
f(X1,X2) = 1/X1

and I was saying to find the marginal density f(X2) you would integrate the above expression

Yes, f(x1,x2) = 1/x1, bit only on an appropriate region in (x1,x2)-space. You need to spell out the details.

Of course you need to do an integral to get the marginal distibution of x2, but that was not the point. I asked what you meant by the weird notation "Integrate(1/x1) from {x2,0,x2}{x1,0,x1}". This looks like something you invented that nobody else knows about, but surely you must have in mind some meaning for it. I am asking you to explain that meaning---in detail, not just saying that you need to integrate. That is: what is the integration variable, and what are the limits of integration?Even better, what is the final answer you get?
 

1. What is a conditional distribution function?

A conditional distribution function is a mathematical function that shows the probability of a certain outcome given that a specific condition is met. It is used to analyze the relationship between two variables and determine how one variable affects the distribution of the other.

2. How is a conditional distribution function different from a regular distribution function?

A regular distribution function, also known as a marginal distribution function, shows the probability of a single variable occurring without considering any other variables. A conditional distribution function, on the other hand, takes into account a specific condition or variable and shows the probability of an outcome given that condition.

3. What is the formula for a conditional distribution function?

The formula for a conditional distribution function is P(Y=y | X=x) = P(Y=y, X=x)/P(X=x), where Y is the dependent variable, X is the independent variable, and P(Y=y, X=x) and P(X=x) are the probabilities of Y and X occurring together and X occurring alone, respectively.

4. How is a conditional distribution function used in data analysis?

A conditional distribution function is used to analyze the relationship between two variables and determine how one variable affects the distribution of the other. It can also be used to identify patterns and trends in data and make predictions about future outcomes.

5. Can a conditional distribution function be used for more than two variables?

Yes, a conditional distribution function can be used for more than two variables. In this case, the formula would be P(Y=y | X=x, Z=z) = P(Y=y, X=x, Z=z)/P(X=x, Z=z), where Z is another variable. This allows for the analysis of multiple variables and their effects on each other's distributions.

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