Transformations and joint pdf's

In summary, the conversation discusses the use of the transformation result to derive an expression for the joint pdf of two random variables in terms of the joint pdf of two other random variables. The conversation also mentions using the Jacobian to generalize the derivative part of the transformation.
  • #1
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Homework Statement



Let X1 and X2 be random variables having a joint pdf, fX1X2(x1,x2). Suppose that Y1=X1X2, and Y2=X1X2 Use the transformation result to derive an expression for the joint pdf of Y1 and Y2
in terms of that for X1 and X2

Homework Equations



The single random variable case

fy(y)=f[g-1(y)] |dg-1(y)/dy|
where g is our transformation


The Attempt at a Solution


So many subscripts,

Anyway I know the single variable case, so how do I generalise this to multiple random variables? Do much the same thing? Let g(Y1,Y2)= (X1 X2,X1/X2) , then what take ∇ .g-1? I'm not really sure how you generalise the derivative part,

Thanks,
 
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  • #2
Think back to calculus when you changed variables from x and y to u=u(x,y) and v=v(x,y) in 2-dimensional integrals. You're doing the same thing here. You need to use the Jacobian.
 
  • #3
I think I see what you mean
so fy(y)= f( g-1(y1,y2)) . Jacobian[ g-1(y1,y2)]
 

1. What are transformations in the context of probability distributions?

Transformations refer to the process of changing the scale or shape of a probability distribution. This can be achieved by applying a mathematical function to the original distribution, resulting in a new distribution with different properties.

2. Why are transformations important in statistics and data analysis?

Transformations are important because they allow us to simplify complex distributions and make them more interpretable. They also help us to meet the assumptions of certain statistical models, making it easier to perform statistical tests and draw conclusions from our data.

3. What is a joint probability distribution?

A joint probability distribution is a probability distribution that describes the likelihood of two or more random variables occurring together. It gives us information about the relationship between these variables and can be used to make predictions or perform statistical analyses.

4. How can we use joint probability distributions to find the probability of events?

To find the probability of events using a joint probability distribution, we can use the formula P(A and B) = P(A) * P(B|A), where P(A) represents the probability of event A, P(B) represents the probability of event B, and P(B|A) represents the conditional probability of event B given that event A has occurred.

5. What is the relationship between transformations and joint probability distributions?

Transformations can be used to modify joint probability distributions, allowing us to simplify or better understand the relationship between multiple variables. In some cases, transformations can also help us to find the joint probability distribution of transformed variables, which can then be used to make predictions or perform analyses.

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