bssm: gaussian_approx – R documentation

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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 class 'nongaussian'
gaussian_approx(model, max_iter = 100, conv_tol = 1e-08, ...)

## S3 method for class '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– 38. https://doi.org/10.1111/sjos.12492

Examples

data("poisson_series")
model <- bsm_ng(y = poisson_series, sd_slope = 0.01, sd_level = 0.1,
  distribution = "poisson")
out <- gaussian_approx(model)

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