bssm: sim_smoother – R documentation

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sim_smoother

Simulation Smoothing

Description

Function sim_smoother performs simulation smoothing i.e. simulates the states from the conditional distribution p(α | y, θ) for linear-Gaussian models.

Usage

sim_smoother(model, nsim, seed, use_antithetic = FALSE, ...)

## S3 method for class 'gaussian'
sim_smoother(
  model,
  nsim = 1,
  seed = sample(.Machine$integer.max, size = 1),
  use_antithetic = FALSE,
  ...
)

## S3 method for class 'nongaussian'
sim_smoother(
  model,
  nsim = 1,
  seed = sample(.Machine$integer.max, size = 1),
  use_antithetic = FALSE,
  ...
)

Arguments

`model`	Model object.
`nsim`	Number of independent samples.
`seed`	Seed for the random number generator.
`use_antithetic`	Use an antithetic variable for location. Default is `FALSE`. Ignored for multivariate models.
`...`	Ignored.

Details

For non-Gaussian/non-linear models, the simulation is based on the approximating Gaussian model.

Value

An array containing the generated samples.

Examples

model <- bsm_lg(rep(NA, 50), sd_level = uniform(1,0,5), sd_y = uniform(1,0,5))
sim <- sim_smoother(model, 12)
ts.plot(sim[, 1, ])

bssm

Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

v1.1.4

GPL (>= 2)

Authors

Jouni Helske [aut, cre] (<https://orcid.org/0000-0001-7130-793X>), Matti Vihola [aut] (<https://orcid.org/0000-0002-8041-7222>)

Initial release

2021-04-13