Solving Joint Convolution for P(X,Y) - 65 Characters

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SUMMARY

The discussion focuses on solving the joint density function P(X,Y) in the context of probability theory. The user successfully demonstrated that P(X) equals P(|Y|) and provided the equations for P(X=x,Y=y) and P(Y=y) using the NormalPDF function. The user expressed a desire to avoid convolutions and Jacobians unless absolutely necessary, indicating a preference for simpler methods. Ultimately, the user resolved the issue after identifying a typo in the original question.

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yamdizzle
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I solved majority of the question I just need to find the last joint density. Found the equations at part 3.

Homework Statement



Show P(X-Y=z ,Y=y) = P(X) = P(|Y|)
I showed P(X) = P(|Y|)

Homework Equations

The Attempt at a Solution


P(X=x,Y=y) = \frac{2*(2x-y)}{\sqrt{2πT^3σ^6}} * exp(((\frac{-(2x-y)^2}{(2σ^2T)}))
P(Y=y) = NormalPDF(0,Tσ^2)
P(X=x) = 2*NormalPDF(0,Tσ^2)

I don't really want to find the convolution then the Jacobian unless I have to. If there is an easier way please let me know.
 
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I took a step ahead and said:

P(X=x, Y=y) = P(X-Y=x-y , Y=y) = P(Z=z, Y=y) but I don't seem to get the right distribution.
 
yamdizzle said:
I solved majority of the question I just need to find the last joint density. Found the equations at part 3.

Homework Statement



Show P(X-Y=z ,Y=y) = P(X) = P(|Y|)
I showed P(X) = P(|Y|)

Homework Equations




The Attempt at a Solution


P(X=x,Y=y) = \frac{2*(2x-y)}{\sqrt{2πT^3σ^6}} * exp(((\frac{-(2x-y)^2}{(2σ^2T)}))
P(Y=y) = NormalPDF(0,Tσ^2)
P(X=x) = 2*NormalPDF(0,Tσ^2)

I don't really want to find the convolution then the Jacobian unless I have to. If there is an easier way please let me know.

Ignore the question There was a typo at the question. I just solved it Thanks
 

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