Exemple #1
0
// NewNormalPrecision creates a new Normal distribution with the given mean and
// precision matrix (inverse of the covariance matrix). NewNormalPrecision
// panics if len(mu) is not equal to prec.Symmetric(). If the precision matrix
// is not positive-definite, NewNormalPrecision returns nil for norm and false
// for ok.
func NewNormalPrecision(mu []float64, prec *mat64.SymDense, src *rand.Rand) (norm *Normal, ok bool) {
	if len(mu) == 0 {
		panic(badZeroDimension)
	}
	dim := prec.Symmetric()
	if dim != len(mu) {
		panic(badSizeMismatch)
	}
	// TODO(btracey): Computing a matrix inverse is generally numerically instable.
	// This only has to compute the inverse of a positive definite matrix, which
	// is much better, but this still loses precision. It is worth considering if
	// instead the precision matrix should be stored explicitly and used instead
	// of the Cholesky decomposition of the covariance matrix where appropriate.
	var chol mat64.Cholesky
	ok = chol.Factorize(prec)
	if !ok {
		return nil, false
	}
	var sigma mat64.SymDense
	sigma.InverseCholesky(&chol)
	return NewNormal(mu, &sigma, src)
}