ekpf_filter: Extended Kalman Particle Filtering

In bssm: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

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 class 'ssm_nlg'
ekpf_filter(
  model,
  particles,
  seed = sample(.Machine$integer.max, size = 1),
  ...
)

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).

Value

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

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)

bssm documentation built on Nov. 2, 2023, 6:25 p.m.