Description Usage Arguments Value References See Also
Particle filtering with sample-resample-move algorithm for multilevel state-space model of Blocker & Airoldi (2011). This has log-normal autoregressive dynamics on OD intensities, log-normal emission distributions, and truncated normal observation densities. This can return full (all particles) output, but it is typically better to aggregate results as you go to reduce memory consumption. It can also run forward or backward filtering for smoothing. These results are combined via a separate function for smoothing; however, this procedure typically performs poorly due to differences between the distributions of particles from forward and reverse filtering.
1 2 3 4 | bayesianDynamicFilter(Y, A, prior, lambda0, sigma0, phi0, rho = 0.1,
tau = 2, m = 1000, verbose = FALSE, Xdraws = 5 * m, Xburnin = m,
Movedraws = 10, nThresh = 10, aggregate = FALSE, backward = FALSE,
tStart = 1)
|
Y |
matrix (n x l) of observed link loads over time, one observation per row |
A |
routing matrix (l x k) for network; must be of full row rank |
prior |
list containing priors for lambda and phi; must have
|
lambda0 |
numeric vector (length k) of time 0 prior means for OD flows |
sigma0 |
numeric vector (length k) of time 0 prior standard deviations for OD flows |
phi0 |
numeric starting value for phi at time 0 |
rho |
numeric fixed autoregressive parameter for dynamics on lambda; see reference for details |
tau |
numeric fixed power parameter for variance structure on truncated normal noise; see reference for details |
m |
integer number of particles to use |
verbose |
logical activates verbose diagnostic output |
Xdraws |
integer number of draws to perform for
|
Xburnin |
integer number of burnin draws to discard
for |
Movedraws |
integer number of iterations to run for each move step |
nThresh |
numeric effective number of independent particles below which redraw will be performed |
aggregate |
logical to activate aggregation of MCMC results; highly |
backward |
logical to activate reverse filtering (for smoothing |
tStart |
integer time index to begin iterations from |
list containing:
xList
lambdaList
phiList
y
rho
prior
n
l
k
A
A_qr
A1
A1_inv
A2
nEff
tStart
backward
aggregate
A.W. Blocker and E.M. Airoldi. Deconvolution of mixing time series on a graph. Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11) 51-60, 2011.
Other bayesianDynamicModel: buildPrior
;
move_step
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