run_mcmc

State space model model of <code>bssm</code> package.

model

iter

Parameters to specific methods. See <code><a rd-options="" href="/link/run_mcmc.gaussian?package=bssm&version=1.1.5" data-mini-rdoc="bssm::run_mcmc.gaussian">run_mcmc.gaussian</a></code>,
<code><a rd-options="" href="/link/run_mcmc.nongaussian?package=bssm&version=1.1.5" data-mini-rdoc="bssm::run_mcmc.nongaussian">run_mcmc.nongaussian</a></code>, <code><a rd-options="" href="/link/run_mcmc.ssm_nlg?package=bssm&version=1.1.5" data-mini-rdoc="bssm::run_mcmc.ssm_nlg">run_mcmc.ssm_nlg</a></code>, 
and <code><a rd-options="" href="/link/run_mcmc.ssm_sde?package=bssm&version=1.1.5" data-mini-rdoc="bssm::run_mcmc.ssm_sde">run_mcmc.ssm_sde</a></code> for details.

Adaptive Markov chain Monte Carlo simulation of state space models using
Robust Adaptive Metropolis algorithm by Vihola (2012). 
See specific methods for various model types for details.

Efficient methods for Bayesian inference of state space models
via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel
importance sampling type weighted estimators
(Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>).
Gaussian, Poisson, binomial, negative binomial, and Gamma
observation densities and basic stochastic volatility models
with linear-Gaussian state dynamics,
as well as general non-linear Gaussian models and discretised
diffusion models are supported.

Jouni Helske

bssm

Bayesian Inference of Non-Linear and Non-Gaussian State Space
Models

Matti Vihola

run_mcmc function

Parameters to specific methods. See <code><a rd-options='' href='run_mcmc.gaussian'>run_mcmc.gaussian</a></code>,
<code><a rd-options='' href='run_mcmc.nongaussian'>run_mcmc.nongaussian</a></code>, <code><a rd-options='' href='run_mcmc.ssm_nlg'>run_mcmc.ssm_nlg</a></code>, 
and <code><a rd-options='' href='run_mcmc.ssm_sde'>run_mcmc.ssm_sde</a></code> for details.

run_mcmc: Bayesian Inference of State Space Models

Description

Usage

Arguments

References