Bootstrap Filtering
Function bootstrap_filter performs a bootstrap filtering with stratification
resampling.
bootstrap_filter(model, particles, ...) ## S3 method for class 'gaussian' bootstrap_filter( model, particles, seed = sample(.Machine$integer.max, size = 1), ... ) ## S3 method for class 'nongaussian' bootstrap_filter( model, particles, seed = sample(.Machine$integer.max, size = 1), ... ) ## S3 method for class 'ssm_nlg' bootstrap_filter( model, particles, seed = sample(.Machine$integer.max, size = 1), ... ) ## S3 method for class 'ssm_sde' bootstrap_filter( model, particles, L, seed = sample(.Machine$integer.max, size = 1), ... )
model |
of class |
particles |
Number of particles. |
... |
Ignored. |
seed |
Seed for RNG. |
L |
Integer defining the discretization level for SDE models. |
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).
Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F, 140, 107–113.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.