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)
| 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 |
The logarithm of the likelihood.