Function particle_smoother performs filter-smoother or forward-backward smoother,
using a either bootstrap filtering or psi-auxiliary filter with stratification resampling.
particle_smoother(object, nsim, ...)# S3 method for gssm
particle_smoother(object, nsim,
seed = sample(.Machine$integer.max, size = 1), ...)
# S3 method for ngssm
particle_smoother(object, nsim, filter_type = "bsf",
seed = sample(.Machine$integer.max, size = 1), max_iter = 100,
conv_tol = 1e-08, ...)
# S3 method for nlg_ssm
particle_smoother(object, nsim, filter_type = "psi",
seed = sample(.Machine$integer.max, size = 1), max_iter = 100,
conv_tol = 1e-08, iekf_iter = 0, ...)
# S3 method for sde_ssm
particle_smoother(object, nsim, L,
seed = sample(.Machine$integer.max, size = 1), ...)
Model.
Number of samples.
Ignored.
Seed for RNG.
Choice of particle filter algorithm. For Gaussian models,
only option is "bsf" (bootstrap particle filter).
In addition, for non-Gaussian or
non-linear models, "psi" uses psi-particle filter, and
for non-linear models options "ekf" (extended Kalman particle filter)
is also available.
Maximum number of iterations used in Gaussian approximation. Used psi-PF.
Tolerance parameter used in Gaussian approximation. Used psi-PF.
If zero (default), first approximation for non-linear
Gaussian models is obtained from extended Kalman filter. If
iekf_iter > 0, iterated extended Kalman filter is used with
iekf_iter iterations.
Integer defining the discretization level.