I've figured out what the issue is here. Basically, there is ambiguity in what
is meant by the covariance matrix.
The getCovariance method in the SingularValueDecomposition class returns a
covariance matrix that could be used to describe the covariance between the
best-fit parameters obtained
As I understand it (which could easily be wrong), calculation of the covariance
(X'X) via SVD follows the following logic:
X = USV'(via SVD, the X' indicates transpose)
X'X = (USV')' USV'
this reduces to
X'X = VSU'USV'
= V S S V'
In the SingularValueDecomposition class the cova
