Questions about Problem Sheet 6, Problem 3d

Questions about Problem Sheet 6, Problem 3d

by Andreea-Alexandra Musat -
Number of replies: 2

Hello, 


I have a question regarding problem 3d from the 6th exercise sheet. 

I have been able to show that  \mathbb{E}[(X - \mathbb{E}[X \vert Z])(Y - \mathbb{E}[Y \vert Z])] = 0 \implies \mathbb{E}_z[Z^2] \mathbb{E}_{xy}[XY] = \mathbb{E}_{xz}[XZ] \mathbb{E}_{yz}[YZ] , but not as shown in the solution (my reasoning was like this:  \mathbb{E}[(X - \mathbb{E}[X \vert Z])(Y - \mathbb{E}[Y \vert Z])] = 0 \implies \mathbb{E}[XY \vert Z] = \mathbb{E}[X \vert Z] \mathbb{E}[Y \vert Z] \implies \int \int xy p(x, y \vert x) dx dy = \int x p(x \vert z) dx \int y p(y \vert z) dy \implies z^2 p(z) \int \int xy p(x, y, z) dx dy =  \int x, z p(x, z) dx \int y z p(y, z) dy which we can integrate to get the conclusion). However, from the way the solution was written, I got the feeling that this should have been more direct, so my question is, is there an easier way of showing that Eq. (35) from the solution pdf holds? 

Thank you!

In reply to Andreea-Alexandra Musat

Re: Questions about Problem Sheet 6, Problem 3d

by Thomas Weinberger -
Dear Andreea,

Here you can simply use the fact that conditional mean = MMSE estimator = linear MMSE estimator in jointly Gaussian case = (8.15) in the script.

Best,

Thomas