bssm (version 2.0.2)

ekpf_filter: Extended Kalman Particle Filtering

Description

Function ekpf_filter performs a extended Kalman particle filtering with stratification resampling, based on Van Der Merwe et al (2001).

Usage

ekpf_filter(model, particles, ...)
# S3 method for ssm_nlg
ekpf_filter(
  model,
  particles,
  seed = sample(.Machine$integer.max, size = 1),
  ...
)

Value

A list containing samples, filtered estimates and the corresponding covariances, weights, and an estimate of log-likelihood.

Arguments

model

Model of class ssm_nlg.

particles

Number of particles as a positive integer. Suitable values depend on the model and the data, and while larger values provide more accurate estimates, the run time also increases with respect to the number of particles, so it is generally a good idea to test the filter first with a small number of particles, e.g., less than 100.

...

Ignored.

seed

Seed for the C++ RNG (positive integer).

References

Van Der Merwe, R., Doucet, A., De Freitas, N., & Wan, E. A. (2001). The unscented particle filter. In Advances in neural information processing systems (pp. 584-590).

Examples

 # Takes a while
set.seed(1)
n <- 50
x <- y <- numeric(n)
y[1] <- rnorm(1, exp(x[1]), 0.1)
for(i in 1:(n-1)) {
 x[i+1] <- rnorm(1, sin(x[i]), 0.1)
 y[i+1] <- rnorm(1, exp(x[i+1]), 0.1)
}

pntrs <- cpp_example_model("nlg_sin_exp")

model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, 
  Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, 
  Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn,
  theta = c(log_H = log(0.1), log_R = log(0.1)), 
  log_prior_pdf = pntrs$log_prior_pdf,
  n_states = 1, n_etas = 1, state_names = "state")

out <- ekpf_filter(model_nlg, particles = 100)
ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3)