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=== Answer 1  ===
 
=== Answer 1  ===
 
I'm not sure what I'm missing on part d) - I know <math>Y_1</math> and <math>Y_2</math> have to have a covariance of zero and that the correlation coefficient is zero for independence.  So I end up with <math>Y=M(X-\mu)</math> and <math>E(Y_1Y_2)=E(Y_1)E(Y_2)</math> but I don't know where to go from there.
 
I'm not sure what I'm missing on part d) - I know <math>Y_1</math> and <math>Y_2</math> have to have a covariance of zero and that the correlation coefficient is zero for independence.  So I end up with <math>Y=M(X-\mu)</math> and <math>E(Y_1Y_2)=E(Y_1)E(Y_2)</math> but I don't know where to go from there.
 +
 +
*<span style="color:green"> Try to diagonalize <math>\Sigma</math>. Did you take linear algebra? -pm </span>
 
=== Answer 2  ===
 
=== Answer 2  ===
 
Write it here.  
 
Write it here.  

Latest revision as of 02:22, 26 March 2013


Practice Problem: Various Questions about a 2D Gaussian


Let

$ X=\left( \begin{array}{l} X_1\\ X_2 \end{array} \right) $

be a two-dimensional Gaussian random variable with mean $ \mu $ and standard deviation matrix $ \Sigma $ given by

$ \mu=\left( \begin{array}{c} -1\\ 2 \end{array} \right) , \Sigma=\left( \begin{array}{cc} 3 & 1 \\ 1 & 3 \end{array} \right) $

a) Write the pdf of X using matrix notation.

b) Write the pdf of X without matrix or vector.

c) Find the marginal pdf for $ X_1 $.

d) Find a matrix M such that the vector $ Y=M(X-\mu) $ consists of independent random variables.

e) Find the joint pdf of Y.


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Answer 1

I'm not sure what I'm missing on part d) - I know $ Y_1 $ and $ Y_2 $ have to have a covariance of zero and that the correlation coefficient is zero for independence. So I end up with $ Y=M(X-\mu) $ and $ E(Y_1Y_2)=E(Y_1)E(Y_2) $ but I don't know where to go from there.

  • Try to diagonalize $ \Sigma $. Did you take linear algebra? -pm

Answer 2

Write it here.

Answer 3

Write it here.


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