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.

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

package-ubuntu-20.04-r-4.0.5#cran#bssm-1.1.4

<p>Prints some basic summaries from the MCMC run by  <code><a href="run_mcmc.html">run_mcmc</a></code>.
</p>

## S3 method for class 'mcmc_output'
print(x, ...)

<table summary="R argblock">
<tr valign="top">
<td><code>x</code></td>
<td>
<p>Output from <code><a href="run_mcmc.html">run_mcmc</a></code>.</p>
</td>
</tr>
<tr valign="top">
<td><code>...</code></td>
<td>
<p>Ignored.</p>
</td>
</tr>
</table>

package-ubuntu-20.04-r-4.0.5#cran#bssm-1.1.4#docs#print.mcmc_output

`x`	Output from `run_mcmc`.
`...`	Ignored.

print.mcmc_output

Description

Usage

Arguments

bssm

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