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.

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

Value

The logarithm of the likelihood.