Hi, 1) No it's a covariance matrix used in a multi-gaussian distribution. Some of the terms are positive and some others are negative. The matrix is symmetric with a dominant diagonal. 2) To inverse the matrix, I wanted to use TRSINV, but you're right, it's only for positive matrices, so it will not work... I need another function... Thank you! Pierre-Luc Drouin On Sat, 12 Jul 2003, Eddy Offermann wrote: > Hi Pierre, > > I got lost in the flurry of questions and replies: > > 1) Is your matrix not positive definite involving density prob. ?? > if so why ?? > 2) Valeri points you to Cholesky decomposition routines which > are no different than the TMatrixD::InvertPosDef() routine. > I would suggest to make use of the TMatrix class function > if you already have your data stored in it. > > Eddy > > > > > Hi, > > > > my matrix is not only positive... > > > > Pierre-Luc Drouin > > > > On Sat, 12 Jul 2003, Eddy Offermann wrote: > > > > > Hi Pierre, > > > > > > Your matrix is probably not only symmetric but also postive definite (x^T A x >= 0) > > > For this case the TMatrixD::InvertPosDef() is the best choice. > > > Determinant is calculated through Double_t TMatrixD::Determinant(). > > > > > > Eddy > > > > > > > > > > > Hello, > > > > > > > > I've to compute the determinant and to invert a matrix in order to > > > > evaluate density probabilities in a multi-gaussian distribution. So the > > > > matrix is symmetric, and diagonal terms are 2-3 order of magnitudes bigger > > > > than off-diagnoal terms. I want to minimize the error of truncature on > > > > determinant and inverse matrix. What should I use instead of > > > > TMatrix::Invert ? > > > > > > > > Thank you! > > > > > > > > Pierre-Luc Drouin > > > > > > > > > >
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