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

In summary, the conversation involved discussing the joint density of two variables, with the goal of finding the joint density of P(X-Y=z, Y=y). The speaker had already solved the majority of the question and found the equations at part 3. They also mentioned not wanting to find the convolution then the Jacobian unless necessary. However, they eventually solved the problem and found the correct distribution.
  • #1
yamdizzle
15
0
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) = [itex]\frac{2*(2x-y)}{\sqrt{2πT^3σ^6}}[/itex] * exp((([itex]\frac{-(2x-y)^2}{(2σ^2T)}[/itex]))
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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  • #2
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.
 
  • #3
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) = [itex]\frac{2*(2x-y)}{\sqrt{2πT^3σ^6}}[/itex] * exp((([itex]\frac{-(2x-y)^2}{(2σ^2T)}[/itex]))
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
 

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

1. What is joint convolution and why is it important in solving P(X,Y)?

Joint convolution is a mathematical operation used to calculate the probability of two random variables occurring together. It is important in solving P(X,Y) because it allows us to model and understand the relationship between two variables.

2. How is joint convolution different from regular convolution?

Regular convolution involves the combination of two functions, while joint convolution involves the combination of two probability distributions. Joint convolution also takes into account the correlation between the two variables, making it more applicable to real-world scenarios.

3. What are some common applications of solving joint convolution for P(X,Y)?

Joint convolution is commonly used in fields such as signal processing, image processing, and machine learning. It can also be used in analyzing data from experiments or surveys, and in modeling complex systems.

4. What are some challenges in solving joint convolution for P(X,Y)?

One of the main challenges in solving joint convolution is finding an appropriate probability distribution to represent the relationship between the two variables. Additionally, the calculations involved in joint convolution can be complex and time-consuming.

5. Are there any limitations to using joint convolution for solving P(X,Y)?

Joint convolution assumes that the two variables are independent, which may not always be the case in real-world scenarios. This can lead to inaccurate results. Additionally, joint convolution may not be suitable for modeling non-linear relationships between variables.

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