weightedVariance

<code>npeaks * npart</code> Matrix of parameter values for each particle.

particles

logarithms of the importance weights of each particle.

log_weights

Vector of weighted means of each particle.

mean

This SMC estimate of the variance can be used to scale the bandwidth of adaptive,
Gaussian random walk Metropolis-Hastings proposals.

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

weightedVariance: Compute the weighted variance of the particles.

Description

Usage

Arguments

Value

See Also