bssm (version 1.1.7-1)

bootstrap_filter: Bootstrap Filtering

Description

Function bootstrap_filter performs a bootstrap filtering with stratification resampling.

Usage

bootstrap_filter(model, particles, ...)
# S3 method for gaussian
bootstrap_filter(
  model,
  particles,
  seed = sample(.Machine$integer.max, size = 1),
  ...
)
# S3 method for nongaussian
bootstrap_filter(
  model,
  particles,
  seed = sample(.Machine$integer.max, size = 1),
  ...
)
# S3 method for ssm_nlg
bootstrap_filter(
  model,
  particles,
  seed = sample(.Machine$integer.max, size = 1),
  ...
)
# S3 method for ssm_sde
bootstrap_filter(
  model,
  particles,
  L,
  seed = sample(.Machine$integer.max, size = 1),
  ...
)

Arguments

model

A model object of class bssm_model.

particles

Number of particles as a positive integer.

...

Ignored.

seed

Seed for RNG (non-negative integer).

L

Positive integer defining the discretization level for SDE models.

Value

List with samples (alpha) from the filtering distribution and corresponding weights (weights), as well as filtered and predicted states and corresponding covariances (at, att, Pt, Ptt), and estimated log-likelihood (logLik).

References

Gordon, NJ, Salmond, DJ, Smith, AFM (1993) Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F, 140(2), p. 107-113.

Examples

# NOT RUN {
set.seed(1)
x <- cumsum(rnorm(50))
y <- rnorm(50, x, 0.5) 
model <- bsm_lg(y, sd_y = 0.5, sd_level = 1, P1 = 1)
  
out <- bootstrap_filter(model, particles = 1000)
ts.plot(cbind(y, x, out$att), col = 1:3)
ts.plot(cbind(kfilter(model)$att, out$att), col = 1:3)

data("poisson_series")
model <- bsm_ng(poisson_series, sd_level = 0.1, sd_slope = 0.01, 
  P1 = diag(1, 2), distribution = "poisson")
  
out <- bootstrap_filter(model, particles = 100)
ts.plot(cbind(poisson_series, exp(out$att[, 1])), col = 1:2)

# }