Update the importance weights of each particle.

Usage

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

Arguments

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.

Value

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.

References

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]