result2

<p>Posterior distribution for pseudo-Voigt parameters, obtained by running
`fitVoigtPeaksSMC` on a Raman spectrum of methanol with 4 peaks.
For details, refer to the vignette.</p>

datasets

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.

Matt Moores

serrsBayes

Bayesian Modelling of Raman Spectroscopy

Jake Carson

Benjamin Moskowitz

Kirsten Gracie

Karen Faulds

Mark Girolami

Engineering and Physical Sciences Research Council 

University of Warwick 

result2 function

Format

SMC particles for methanol (CH3OH) — result2

SMC particles for methanol (CH3OH)

result2: SMC particles for methanol (CH3OH)

Description

Usage

Arguments

Format