sim_smoother: Simulation Smoothing

Description Usage Arguments Details Value Examples

View source: R/sim_smoother.R

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

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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

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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 documentation built on July 10, 2021, 9:07 a.m.