Hi Gael, I took the time to dig a little bit, and found some MATLAB code written by Diego di Bernardo.
http://dibernardo.tigem.it/wiki/index.php/Network_Inference_by_Reverse-engineering_NIR He has a closed form result (the formula is in calc_cov.m), but the formula is really weird, and I can't really figure out where it comes from. function covA = calc_cov(A,X,sX,P,sP,RIDGE,W); % covA = calc_cov(A,X,sX,P,sP [, RIDGE, W]); % X,sX,P,SP are N x M, where N=number of genes, M=number of expts. % RIDGE is an optional ridge regression parameter % W is an optional weight parameter. [rows,N]=size(A); covA=zeros(N,N,rows); [N,M] = size(X); if 1~=exist('RIDGE') RIDGE = 0; end if 1~=exist('W') W = eye(M); end Q = W*W'; for g=1:rows idx = find(A(g,:)~=0); vEta = sP(g,:).^2 + A(g,:).^2 * sX.^2; % vEta = sP(g,g).^2 + A(g,:).^2 * sX.^2; % Deigo's way Z=X(idx,:); T=inv(Z*Q*Z'+RIDGE*eye(length(idx)))*Z*Q'; covA(idx,idx,g) = T*diag(vEta)*T'; end I'm not really sure how exactly the formula was derived though. Note - while the code is free to download, he specifically says that he doesn't want the code used for commercial purposes without permission. I presume reproducing it is ok? Federico On Mon, Oct 22, 2012 at 3:34 PM, Gael Varoquaux <gael.varoqu...@normalesup.org> wrote: > On Fri, Oct 19, 2012 at 01:09:21PM +0200, federico vaggi wrote: >> Assuming that X and B are experimentally measured values with >> uncertainties, what's the correct way to transfer that uncertainty to >> A? > > There exists to my knowledge no theoretical/closed form result. I would > rely on bootstrap: > http://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29 > > G > > ------------------------------------------------------------------------------ > Everyone hates slow websites. So do we. > Make your web apps faster with AppDynamics > Download AppDynamics Lite for free today: > http://p.sf.net/sfu/appdyn_sfd2d_oct > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ Everyone hates slow websites. So do we. Make your web apps faster with AppDynamics Download AppDynamics Lite for free today: http://p.sf.net/sfu/appdyn_sfd2d_oct _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
