reWeightParticles {serrsBayes}

Update the importance weights of each particle.

Description

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]