PDF of Matrix Transformation

In summary: With matrix A and vector b, the Jacobian matrix is given by |A|. Therefore, the pdf of Y=AX+b is f(y)=f(x)|A|, where f(x) is the original pdf. In summary, the pdf of Y can be derived in terms of the original pdf f(x) by multiplying it with the determinant of the Jacobian matrix between x and y.
  • #1
alpines4
4
0

Homework Statement


Define X to be an n-vector of jointly continuous random variables X1, ..., Xn with joint pdf f(x) mapping R^n to R. Let A be an invertible nxn matrix and set Y=AX+b. I want to derive the pdf of f(y) in terms of f(x), the original pdf.



Homework Equations






The Attempt at a Solution



Given a random variable and its PDF f(x), the transformation of Y=g(X) is (given that g is one to one and thus has an inverse) f(g^{-1}(y)) * g'(y). I don't know how to generalize this to a matrix, however. I assume it will be kind of similar... Any help is appreciated. I just need some tips to get started. Thank you!
 
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  • #2
alpines4 said:

Homework Statement


Define X to be an n-vector of jointly continuous random variables X1, ..., Xn with joint pdf f(x) mapping R^n to R. Let A be an invertible nxn matrix and set Y=AX+b. I want to derive the pdf of f(y) in terms of f(x), the original pdf.



Homework Equations






The Attempt at a Solution



Given a random variable and its PDF f(x), the transformation of Y=g(X) is (given that g is one to one and thus has an inverse) f(g^{-1}(y)) * g'(y). I don't know how to generalize this to a matrix, however. I assume it will be kind of similar... Any help is appreciated. I just need some tips to get started. Thank you!


Intuitively the pdf of Y=g(X) is given by
[tex]f_X(x)|dx|=f_Y(y)|dy|[/tex]
In the case of vector x and y, dx and dy is related through the determinant of the Jacobian matrix between x and y
 

1. What is a PDF of Matrix Transformation?

A PDF (Probability Density Function) of Matrix Transformation is a mathematical representation of the probability distribution of a matrix transformation. It describes the probability of a random variable being within a certain range of values after undergoing a matrix transformation.

2. How is a PDF of Matrix Transformation calculated?

The PDF of Matrix Transformation is calculated by multiplying the original PDF by the Jacobian determinant of the transformation matrix. This accounts for any changes in the size and shape of the probability distribution after the transformation.

3. What is the significance of the PDF of Matrix Transformation in statistics?

The PDF of Matrix Transformation is significant in statistics as it allows for the analysis of how a probability distribution changes after a transformation. This is important in various fields such as machine learning, signal processing, and image processing.

4. Can the PDF of Matrix Transformation be used for non-linear transformations?

Yes, the PDF of Matrix Transformation can be used for non-linear transformations. However, in these cases, the transformation matrix may no longer be a square matrix and the Jacobian determinant may need to be calculated differently.

5. Are there any limitations to using the PDF of Matrix Transformation?

One limitation of the PDF of Matrix Transformation is that it assumes the transformation is invertible, meaning that it can be reversed. This may not always be the case, especially for non-linear transformations. Additionally, the PDF may not accurately reflect the true probability distribution if the transformation is complex.

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