summary.mcmc_output

object

if <code>FALSE</code> (default), computation of standard 
errors and effective sample sizes is omitted.

return_se

Are the summary statistics computed for either <code>"theta"</code> (default), 
<code>"states"</code>, or <code>"both"</code>?

variable

Deprecated. If <code>TRUE</code>, summaries are computed only for hyperparameters theta.

only_theta

This functions returns a list containing mean, standard deviations, standard errors, and 
effective sample size estimates for parameters and states.

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, <arXiv:1609.02541>).
Gaussian, Poisson, binomial, negative binomial, and Gamma
observation densities and basic stochastic volatility models with Gaussian state
dynamics, as well as general non-linear Gaussian models and discretised
diffusion models are supported.

summary.mcmc_output: Summary of MCMC object

Description

Usage

Arguments