importanceSSM: Importance Sampling of Non-Gaussian State Space Model

In jrnold/KFAS: Kalman Filter and Smoother for Exponential Family State Space Models.

Description

Importance Sampling of Non-Gaussian State Space Model.

Usage

  importanceSSM(model, nsim = 1000, save.model = FALSE,
    theta = NULL, antithetics = TRUE, maxiter = 100)

Arguments

model	Non-Gaussian state space model object of class SSModel.
nsim	Number of independent samples. Default is 1000.
save.model	Return the original model with the samples. Default is FALSE.
theta	Initial values for conditional mode theta. Default is log(mean(y/u)) for Poisson and log(mean(y/(u-y))) for Binomial distribution (or log(mean(y)) in case of u[t]-y[t] = 0 for some t).
antithetics	Logical. If TRUE, two antithetic variables are used in simulations, one for location and another for scale. Default is TRUE.
maxiter	Maximum number of iterations used in linearisation. Default is 100.

Details

Function importanceSSM simulates states of the non-Gaussian state space model conditioned with the observations, returning the simulated samples of the states with the importance weights.

jrnold/KFAS documentation built on May 19, 2019, 11:55 p.m.