print.mcmc_output

Prints some basic summaries from the MCMC run by <code>run_mcmc</code>.

Efficient methods for Bayesian inference of state space models
via Markov chain Monte Carlo (MCMC) based on parallel
importance sampling type weighted estimators
(Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>),
particle MCMC, and its delayed acceptance version.
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.
See Helske and Vihola (2021, <doi:10.32614/RJ-2021-103>) for details.

Jouni Helske

bssm

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

Matti Vihola

print.mcmc_output function

<dl><dt>x</dt>
<dd>Object of class <code>mcmc_output</code> from <code>run_mcmc</code>.</dd>
<dt>...</dt>
<dd>Ignored.</dd></dl>

Arguments

Print Results from MCMC Run — print.mcmc_output

<dl>

<dt>x</dt>
<dd>Object of class <code>mcmc_output</code> from <code>run_mcmc</code>.</dd>


<dt>...</dt>
<dd>Ignored.</dd>

</dl>

print.mcmc_output: Print Results from MCMC Run

Description

Usage

Arguments

Examples