Take the 2-minute tour ×
Mathematics Stack Exchange is a question and answer site for people studying math at any level and professionals in related fields. It's 100% free, no registration required.

Task: Suppose we model a variable $y = Wx + \mu$ as a linear transformation of $x$ plus some Gaussian noise $\mu\sim\mathcal N(0,\sigma I)$. Our aim is to minimize the estimation error of $x$ given $y$ in terms of $W$, i.e. we want to minimize the entropy $H(x|y,W)$ as a function of $W$. Suppose that, during learning, we know $x$ for every observed state $y$: what is the optimal supervised update of the model parameters $W$?

The problem is: I can't wrap my head around the question how I come - in a rigorous way - from the estimation problem that I want to solve to the parameter updates that depend on the variable I want to estimate. It's probably a standard procedure but the problem is hard to nail down to Google-friendly buzzwords.

Thanks in advance!! blue2script

share|improve this question

1 Answer 1

I dont have answer to your question but i am interested in knowing what exactly does this statement in your question mean :

"Our aim is to minimize the estimation error of x given y in terms of W, i.e. we want to minimize the entropy $H(x|y,W)$ as a function of W"

share|improve this answer

Your Answer

 
discard

By posting your answer, you agree to the privacy policy and terms of service.

Not the answer you're looking for? Browse other questions tagged or ask your own question.