reWeightParticles: Update the importance weights of each particle.
In serrsBayes: Bayesian Modelling of Raman Spectroscopy

Description Usage Arguments Value References

Update the importance weights of each particle.

reWeightParticles(
  spectra,
  peaks,
  baselines,
  i,
  start,
  sigma,
  old_weights,
  alpha,
  idx
)

`spectra`	`n_y * nwl` Matrix of observed Raman spectra.
`peaks`	`nwl * npart` Matrix containing the spectral signatures for each observation.
`baselines`	`nwl * npart` Matrix containing the current values of the baselines.
`i`	index of the current observation to use in calculating the likelihood
`start`	index of the next wavelength to use in calculating the likelihood, permuted by `idx`
`sigma`	Vector of `npart` standard deviations for each particle.
`old_weights`	logarithms of the importance weights of each particle.
`alpha`	the target learning rate for the reduction in effective sample size (ESS).
`idx`	permutation of the indices of the wavelengths.

a List containing:

ess: The effective sample size, after reweighting.
weights: Vector of updated importance weights.
index: index of the last wavelength used.
evidence: SMC estimate of the logarithm of the model evidence.

Pitt, dos Santos Silva, Giordani & Kohn (2012) "On some properties of Markov chain Monte Carlo simulation methods based on the particle filter" J. Econometrics 171(2): 134–151, DOI: doi: 10.1016/j.jeconom.2012.06.004

Zhou, Johansen & Aston (2015) "Towards Automatic Model Comparison: An Adaptive Sequential Monte Carlo Approach" arXiv:1303.3123 [stat.ME]

serrsBayes documentation built on June 28, 2021, 5:14 p.m.