expand_sample

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>.

Expand parameters <code>"theta"</code> or states <code>"states"</code>.

variable

Vector of indices. In case of states, 
what time points to expand? Default is all.

times

Vector of indices. In case of states, 
what states to expand? Default is all.

states

If <code>TRUE</code> (default), return list by states. 
Otherwise by time.

by_states

The MCMC algorithms of <code>bssm</code> use a jump chain representation where we 
store the accepted values and the number of times we stayed in the current 
value. Although this saves bit memory and is especially convenient for 
IS-corrected MCMC, sometimes we want to have the usual sample paths. 
Function <code>expand_sample</code> returns the expanded sample based on the 
counts. Note that for IS-corrected output the expanded 
sample corresponds to the approximate posterior.

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

expand_sample function

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

expand_sample: Expand the Jump Chain representation

Description

Usage

Arguments