Its exactly what I derived myself, so I understand it :) But it might be difficult for causal reader.
My suggestions: - you could add factor graph to ease thinking about it. - [most important] describe what x, sigma_i, and u_i are - [important] you could explicitly state bayes theorem to derive posteriori f(x). - rename f(x) to P(X = x) or density p(x) - you can comment that mean is at the mode (peak) as posterior likelihood is - you should state what RAVE estimator is, and why it is biased - [important] you should state your final estimator that is alternative to RAVE - experimental setup would be useful :) Lukasz 2008/9/23 Jason House <[EMAIL PROTECTED]>: > On Mon, Sep 22, 2008 at 1:21 PM, Łukasz Lew <[EMAIL PROTECTED]> wrote: >> >> Hi, >> >> On Mon, Sep 22, 2008 at 17:58, Jason House <[EMAIL PROTECTED]> >> wrote: >> > On Sep 22, 2008, at 7:59 AM, Magnus Persson <[EMAIL PROTECTED]> >> > wrote: >> > >> > The results of the math are most easilly expressed in terms of inverse >> > variance (iv=1/variance) >> > >> > Combined mean = sum( mean * iv ) >> > Combined iv = sum( iv ) >> > >> > I'll try to do a real write-up if anyone is interested. >> >> I am very interested. :) >> >> Lukasz > > > Attached is a quick write up of what I was talking about with some math. > > PS: Any tips on cleanup and making it a mini publication would be > appreciated. I've never published a paper before. Would this be too small? >
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