Re: [ROOT] Compute the determinant and invert a covariance matrix

From: Eddy Offermann (eddy@rentec.com)
Date: Sun Jul 13 2003 - 02:42:26 MEST


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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