Description Usage Arguments Value References
Update the importance weights of each particle.
1 2 3 4 5 6 7 8 9 10 11  reWeightParticles(
spectra,
peaks,
baselines,
i,
start,
sigma,
old_weights,
alpha,
idx
)

spectra 

peaks 

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 
sigma 
Vector of 
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]
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