Statistics/Probability: Joint Densities

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In summary, the general way to solve problems such as these is to let X and Y have the joint pdf f_X,Y(x,y) = 2e^-(x+y) , 0 < x < y, 0 < y. You would then find P(Y < 3X) by taking the integral from 1/3Y to Y wrt X and P(X < 2Y) by taking the integral from 0 to 2y wrt x.
  • #1
ak416
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Ok Whats the general way to solve problems such as these?
-Let X and Y have the joint pdf f_X,Y(x,y) = 2e^-(x+y) , 0 < x < y, 0 < y
Find P(Y < 3X).
-Find P(X < 2Y) if f_X,Y(x,y) = x + y for X and Y defined over the unit interval (meaning from 0 to 1).
I know you have to set up the double integral, like for the first one the answer is:
answer1.GIF

but P(Y < 3X) => P(x > 1/3Y) so why can't you take the integral from 1/3Y to Y wrt X (because x has to be smaller than y) and the integral from 0 to infinity with respect to y (because y has to be greater than 0)?
Note:I am currently taking a multivariable calculus course and I haven't gotten to multi-integration yet, so i would just like to know the general method for determining the boundaries of integration.
I also have other questions (about Transforming random variables) but ill save those for later, I would like to understand this first.
Thanks.
 
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  • #2
You can. And you would get the same answer.

In the xy-plane, 0< x< y, 0< y is infinite triangle between the y-axis and the line y= x. In order to be a probability density over that region, the integral of 2e^-(x+y) over that region must be 1.
In subset of that region such that y< 3x is the infinite triangle between the lines y= 3x and y= x. P(Y< 3X) is the integeral of 2e^-(x+y) over that region.
There are always two ways of setting up a double integral over a region like that. We can take the outer integral in y taking y going from 0 to infinity. For each such y, a horizontal line in that region must go from y= 3x to y=x which is equivalent to x ranging from y/3 up to y. The probability is
[tex]2\int_{y= 0}^\infty\int_{x= y/3}^y e^{-(x+y)}dxdy[/tex]

But you could also take the outer integral to be in terms of x, with x running form 0 to infinity. In that case, for each x, a vertical line covering the region would have y running from x to 3x. The probability is
[tex]2\int_{x=0}^\infty\int_{y= x}^{3x} e^{-(x+y)}dydx[/tex]
 
  • #3
Thanks, that really helped me visualize what's going on. But what about about the second problem, find P(X < 2y)... either my computations are wrong or integral from 0 to 1 wrt y and integral from 0 to 2y wrt x (with the pdf in between) is wrong. (Gives me a probability of 4/3). and Why?
 

1. What is a joint probability density function?

A joint probability density function is a function that describes the relationship between two or more random variables. It assigns a probability to each possible combination of values for the variables, allowing us to calculate the probability of a particular outcome occurring.

2. How is a joint probability density function different from a marginal probability density function?

A joint probability density function considers the relationship between multiple variables, while a marginal probability density function only looks at the probability of one variable. Marginal probability density functions are obtained by integrating the joint probability density function over all possible values of the other variables.

3. Can the joint probability density function be used for discrete and continuous variables?

Yes, the joint probability density function can be used for both discrete and continuous variables. For discrete variables, it is called a joint probability mass function, and for continuous variables, it is called a joint probability density function.

4. How is the joint probability density function related to conditional probability?

The joint probability density function can be used to calculate conditional probabilities, which is the probability of an event occurring given that another event has already occurred. This can be done by dividing the joint probability density function by the marginal probability density function of the given event.

5. What is the relationship between the joint probability density function and the cumulative distribution function?

The cumulative distribution function can be obtained by integrating the joint probability density function over all possible values of the random variables. It represents the probability that the random variables are less than or equal to a certain value.

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