bssm (version 1.1.5)

gaussian_approx: Gaussian Approximation of Non-Gaussian/Non-linear State Space Model

Description

Returns the approximating Gaussian model. This function is rarely needed itself, and is mainly available for testing and debugging purposes.

Usage

gaussian_approx(model, max_iter, conv_tol, ...)
# S3 method for nongaussian
gaussian_approx(model, max_iter = 100, conv_tol = 1e-08, ...)
# S3 method for ssm_nlg
gaussian_approx(model, max_iter = 100, conv_tol = 1e-08, iekf_iter = 0, ...)

Arguments

model

Model to be approximated.

max_iter

Maximum number of iterations.

conv_tol

Tolerance parameter.

...

Ignored.

iekf_iter

For non-linear models, number of iterations in iterated EKF (defaults to 0).

References

Koopman, S.J. and Durbin J. (2012). Time Series Analysis by State Space Methods. Second edition. Oxford: Oxford University Press. Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 2020; 1<U+2013> 38. https://doi.org/10.1111/sjos.12492

Examples

# NOT RUN {
data("poisson_series")
model <- bsm_ng(y = poisson_series, sd_slope = 0.01, sd_level = 0.1,
  distribution = "poisson")
out <- gaussian_approx(model)
# }