Bayesian Modelling of Raman Spectroscopy // Sequential Monte
Carlo (SMC) algorithms for fitting a generalised additive mixed
model (GAMM) to surface-enhanced resonance Raman spectroscopy
(SERRS), using the method of Moores et al. (2016)
arXiv:1604.07299. Multivariate observations of SERRS are highly
collinear and lend themselves to a reduced-rank representation.
The GAMM separates the SERRS signal into three components: a
sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a
smoothly-varying baseline; and additive white noise. The
parameters of each component of the model are estimated
iteratively using SMC. The posterior distributions of the
parameters given the observed spectra are represented as a
population of weighted particles.