Recent content by bakav

thanks dude appreciate it. Can you guide me that how you got, in your derivation, from = exp(tr(log(A+O'XO))) dXtr(log(A+O'XO) = det(A+O'XO) tr(dXlog(A+O'XO)) = det(A+O'XO) tr((A+O'XO)-1dX(A+O'XO)) = det(A+O'XO) O(A+O'XO)-1O' My question addresses how did you bring in the partial derivative dX...

Thank you man. That was a good help. I'm just concerned about the derivation with respect to G. Actually, in my formulation inv(G) is a symmetric matrix do you think that the derivation will change in this case?

Thanks man. Actually I'm just trying to maximize the marginal likelihood function by setting the first derivative to zero. The log of the marginal likelihood is (I'm just writing down the terms containing the parameter that should be estimated): -ln(det(A+O'inv(G)O))+ln(det(inv(G))+f'*inv(G)*f...

Hi, How can I solve the equation below for M. G*inv(A+G'*inv(M)*G)*G'+F+M=0 G' is the transpose of G and inv(.) is the inverse of a matrix. Thanks

Thanks man I also tried to use the general formula as: d(det(Y))/dX=dtr(QY)/dX where Q is the det(Y)inv(Y) and apparently Y is a function of X. I got the same derivation as you did. Thanks a lot for your great help.

Hi there, I want to derive the derivative of the det(A+O'XO) with respect to X, where A, O', O and X are all matrix. Any suggestions, Thanks Baska