importance_sample: Importance Sampling from non-Gaussian State Space Model

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

Importance Sampling from non-Gaussian State Space Model

Description

Returns nsim samples from the approximating Gaussian model with corresponding (scaled) importance weights. Probably mostly useful for comparing KFAS and bssm packages.

Usage

importance_sample(model, nsim, use_antithetic, max_iter, conv_tol, seed, ...)

## S3 method for class 'nongaussian'
importance_sample(
  model,
  nsim,
  use_antithetic = TRUE,
  max_iter = 100,
  conv_tol = 1e-08,
  seed = sample(.Machine$integer.max, size = 1),
  ...
)

Arguments

model	Model of class bsm_ng, ar1_ng svm, ssm_ung, or ssm_mng.
nsim	Number of samples (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 to the number of samples, so it is generally a good idea to test the filter first with a small number of samples, e.g., less than 100.
use_antithetic	Logical. If TRUE (default), use antithetic variable for location in simulation smoothing. Ignored for ssm_mng models.
max_iter	Maximum number of iterations as a positive integer. Default is 100 (although typically only few iterations are needed).
conv_tol	Positive tolerance parameter. Default is 1e-8. Approximation is claimed to be converged when the mean squared difference of the modes of is less than conv_tol.
seed	Seed for the C++ RNG (positive integer).
...	Ignored.

Examples

data("sexratio", package = "KFAS")
model <- bsm_ng(sexratio[, "Male"], sd_level = 0.001, 
  u = sexratio[, "Total"],
  distribution = "binomial")

imp <- importance_sample(model, nsim = 1000)

est <- matrix(NA, 3, nrow(sexratio))
for(i in 1:ncol(est)) {
  est[, i] <- diagis::weighted_quantile(exp(imp$alpha[i, 1, ]), imp$weights, 
    prob = c(0.05,0.5,0.95))
}

ts.plot(t(est),lty = c(2,1,2))

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