print.mcmc_output

Output from <code><a rd-options="" href="/link/run_mcmc?package=bssm&version=1.1.7-1" data-mini-rdoc="bssm::run_mcmc">run_mcmc</a></code>.

Prints some basic summaries from the MCMC run by <code><a rd-options="" href="/link/run_mcmc?package=bssm&version=1.1.7-1" data-mini-rdoc="bssm::run_mcmc">run_mcmc</a></code>.

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

print.mcmc_output function

Output from <code><a rd-options='' href='run_mcmc'>run_mcmc</a></code>.

Prints some basic summaries from the MCMC run by <code><a rd-options='' href='run_mcmc'>run_mcmc</a></code>.

print.mcmc_output: Print Results from MCMC Run

Description

Usage

Arguments