Hi, So I'll use this function. To compute the determinant of this kind of matrix, what should I use? Thank you! Pierre-Luc Drouin On Mon, 14 Jul 2003, Eddy Offermann wrote: > Hi Pierre, > > > 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. > > So your density distribution of the joint normal distribution is of the form > > prob = constant exp(-1/2 (x-a)^T B (x-a)) > > where x is your n-component vector and C = Invert(B) is the covariance matrix. > > B (and therefore C) IS positive definite so that y^T B y >=0 for any y !! > > > > > 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... > > > > NO you can use TMatrix::InverPosdef() > > Eddy > > > 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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