Joint PDF of Random Variables X & Y -1 to 1

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The discussion focuses on the joint probability density function (PDF) of random variables X and Y defined as fx,y(x, y) = 1/2 for the range -1 ≤ x ≤ y ≤ 1, and 0 otherwise. Participants seek assistance in deriving the marginal PDF fy(y), the conditional PDF fx|y(x|y), and the expected value E[X|Y = y]. There is a specific inquiry about the limits for integration needed to calculate fy(y). The conversation emphasizes the need for clarity in the integration process to solve the problems effectively.
vptran84
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Hi, I really need help with joint PDF, if anyone can help, that would be super! :smile:

Random Variables X and Y have joint PDF
fx,y (x, y) = 1/2 if -1 <= x <=y <= 1, and it is 0 otherwise

a) what is fy (y)?

b) what is fx|y (x|y)?

c) what is E[X|Y = y]?
 
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Please show some work, first.
 
for part A) i know ur suppose to take the integral with respect to dx, but I'm not sure what the limits are.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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