Update the importance weights of each particle.

Update the importance weights of each particle.

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: 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]