This is an internal function that is only exposed on the public API for unit testing purposes. It computes the log-likelihood of the spline and the noise, once the spectral signature has been subtracted from the observed data. Thus, it can be used with either Lorentzian, Gaussian, or pseudo-Voigt broadening functions.
Usage
computeLogLikelihood(
obsi,
lambda,
prErrNu,
prErrSS,
basisMx,
eigVal,
precMx,
xTx,
aMx,
ruMx
)Arguments
- obsi
Vector of residuals after the spectral signature has been subtracted.
- lambda
smoothing parameter of the penalised B-spline.
- prErrNu
hyperparameter of the additive noise
- prErrSS
hyperparameter of the additive noise
- basisMx
Matrix of B-spline basis functions
- eigVal
eigenvalues of the Demmler-Reinsch factorisation
- precMx
precision matrix for the spline
- xTx
sparse matrix cross-product
- aMx
orthoganal matrix A from the Demmler-Reinsch factorisation
- ruMx
product of Ru from the Demmler-Reinsch factorisation