Function particle_smoother performs filter-smoother or forward-backward smoother,
using a either bootstrap filtering or psi-auxiliary filter with stratification resampling.
particle_smoother(model, nsim, ...)# S3 method for nongaussian
particle_smoother(
model,
nsim,
method = "psi",
seed = sample(.Machine$integer.max, size = 1),
max_iter = 100,
conv_tol = 1e-08,
...
)
# S3 method for ssm_nlg
particle_smoother(
model,
nsim,
method = "psi",
seed = sample(.Machine$integer.max, size = 1),
max_iter = 100,
conv_tol = 1e-08,
iekf_iter = 0,
...
)
# S3 method for ssm_sde
particle_smoother(
model,
nsim,
L,
seed = sample(.Machine$integer.max, size = 1),
...
)
Model.
Number of samples.
Ignored.
Choice of particle filter algorithm.
For Gaussian and non-Gaussian models with linear dynamics,
options are "bsf" (bootstrap particle filter)
and "psi" (\(\psi\)-APF, the default), and
for non-linear models options "ekf" (extended Kalman particle filter)
is also available.
Seed for RNG.
Maximum number of iterations used in Gaussian approximation. Used \(\psi\)-APF.
Tolerance parameter used in Gaussian approximation. Used \(\psi\)-APF.
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.