The density of Y only depends on y.
But the conditional will have 0<y<x
You're missing the exponent in the first integration.
And you should compute k immediately.
you have lots of errors in your integration for E(X|Y) like the x.
Question. Let X and Y be random variables with joint density
Derive the conditional density, , and the conditional expectation, E[X|Y]. Hence or otherwise, evaluate E(X) and Cov(X,Y).
(I highlighted the questions that came up along the answer write up by ->>>>>)
->>>>>>>Is this support right, or should I say (ie include x in it)
Then expected value
"Hence" somehow hint on using E(X|Y) in evaluating E(X), and I try that:
->>>>>>So still need to do the integration to find E(X) directly from the integral, or E(Y) first and then deduct 1 to get E(X). Right?
I find E(Y) since the integration is slightly easier.
but I probably messed up signs somewhere and I feel it should be k... (no big deal, I'll recheck).
->>>>>The question is, do I need to take into account the bound on y which is y<x?
Once I get E(Y), .
Then and again I need to integrate to find E(XY), right? Or there is an easy way...
I agree. I need to recompute because I took the bounds for x starting from y, not from 0 (ie as in conditional density bounds). So the y density is not right, therefore, the conditional x|y density above is not right.
Updated: No, I think I was right in the first calculation, x is bounded by y from below and therefore it belongs there in the calculation of .
By the way, why do you say calculate k (why not lambda, for example)? Updated - see k calculation below.
Fine, I'll look at it.
WE just had 20 inches of snow and my car is buried..........
The street won't be cleared for days nor my parking lot.
So clearing my car is not even the point.
and there's no reason to do parts on that.
After the first integration you should let and use the gamma function.
Then when y>0.
So Then when x>y>0.
My answer is
By the way I did this in about 2 minutes, here's my work.
Watch how I do NOT integrate...
This answer makes sense, since it exceeds Y and it depends on lambda.
Well, what can I say, you are worth your name! I have never been friends with Gamma function, not since the calculus days. I will re-do the integration again, now that I know the right answer. Hope you recover your car. It's a glorious day here with +18C and sunny, and all I am jumping between distribution theory and linear algebra with no end in sight.
(Also, isn't it strange that and both equal to the same number, 1? understand that this comes from and that 0!=1 by convention, but I still find it very strange.)
By the way, I did another (non-Gamma) integration but with some substition and I got to which is still not correct. But I'll rechecked my signs and I still cannot figure out where I make mistake. I'll keep trying, binomially, until success...
Now, taking ...
now trying out the Gamma integral and setting , then
Does it make sense?
To find Cov (X,Y), looks like I still need to integrate more, to find E(XY) or E(Y^2):
question - can I do that E(XY)=E(YX)?
can be dealt with again using Gamma function (y^(3-1))
Saw the Chicago snowstorm news on TV today. How do you guys move around? the roads are just full of snow, looks like nothing moves.