gaussian_approx  R Documentation 
Returns the approximating Gaussian model which has the same conditional mode of p(alphay, theta) as the original model. This function is rarely needed itself, and is mainly available for testing and debugging purposes.
gaussian_approx(model, max_iter, conv_tol, ...) ## S3 method for class 'nongaussian' gaussian_approx(model, max_iter = 100, conv_tol = 1e08, ...) ## S3 method for class 'ssm_nlg' gaussian_approx(model, max_iter = 100, conv_tol = 1e08, iekf_iter = 0, ...)
model 
Model to be approximated. Should be of class

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 1e8. Approximation
is claimed to be converged when the mean squared difference of the modes of
is less than 
... 
Ignored. 
iekf_iter 
For nonlinear models, nonnegative number of iterations in
iterated EKF (defaults to 0, i.e. normal EKF). Used only for models of class

Returns linearGaussian SSM of class ssm_ulg
or
ssm_mlg
which has the same conditional mode of p(alphay, theta) as
the original model.
Koopman, SJ and Durbin J (2012). Time Series Analysis by State Space Methods. Second edition. Oxford: Oxford University Press.
Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 138. https://doi.org/10.1111/sjos.12492
data("poisson_series") model < bsm_ng(y = poisson_series, sd_slope = 0.01, sd_level = 0.1, distribution = "poisson") out < gaussian_approx(model) for(i in 1:7) cat("Number of iterations used: ", i, ", y[1] = ", gaussian_approx(model, max_iter = i, conv_tol = 0)$y[1], "\n", sep ="")
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