# importance_sample: Importance Sampling from non-Gaussian State Space Model In bssm: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

## Description

Returns nsim samples from the approximating Gaussian model with corresponding (scaled) importance weights. Probably mostly useful for comparing KFAS and bssm packages.

  1 2 3 4 5 6 7 8 9 10 11 12 importance_sample(model, nsim, use_antithetic, max_iter, conv_tol, seed, ...) ## S3 method for class 'nongaussian' importance_sample( model, nsim, use_antithetic = TRUE, max_iter = 100, conv_tol = 1e-08, seed = sample(.Machine$integer.max, size = 1), ... )  ## Arguments  model of class bsm_ng, ar1_ng svm, ssm_ung, or ssm_mng. nsim Number of samples. use_antithetic Logical. If TRUE (default), use antithetic variable for location in simulation smoothing. Ignored for ssm_mng models. max_iter Maximum number of iterations used for the approximation. conv_tol Convergence threshold for the approximation. Approximation is claimed to be converged when the mean squared difference of the modes is less than conv_tol. seed Seed for the random number generator. ... Ignored. ## Examples   1 2 3 4 5 6 7 8 9 10 11 12 13 data("sexratio", package = "KFAS") model <- bsm_ng(sexratio[, "Male"], sd_level = 0.001, u = sexratio[, "Total"], distribution = "binomial") imp <- importance_sample(model, nsim = 1000) est <- matrix(NA, 3, nrow(sexratio)) for(i in 1:ncol(est)) { est[, i] <- Hmisc::wtd.quantile(exp(imp$alpha[i, 1, ]), imp\$weights, prob = c(0.05,0.5,0.95), normwt=TRUE) } ts.plot(t(est),lty = c(2,1,2)) 

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