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