student_t_control

Set (MCMC) computational options for a Student-t likelihood model

Fit multi-level models with possibly correlated
random effects using Markov Chain Monte Carlo simulation.
Such models allow smoothing over space and time and are useful in,
for example, small area estimation.

Harm Jan Boonstra

mcmcsae

Markov Chain Monte Carlo Small Area Estimation

Grzegorz Baltissen

student_t_control function

<dl><dt>proposal</dt>
<dd>"RW" for random walk Metropolis-Hastings or "mala"
for Metropolis-adjusted Langevin.</dd>
<dt>MHscale</dt>
<dd>(starting) scale of Metropolis-Hastings update.</dd>
<dt>adapt</dt>
<dd>whether to adapt the scale of the proposal distribution
during burnin to achieve better acceptance rates.</dd></dl>

Arguments

Set (MCMC) computational options for a Student-t likelihood model — student_t_control

<dl>

<dt>proposal</dt>
<dd>"RW" for random walk Metropolis-Hastings or "mala"
for Metropolis-adjusted Langevin.</dd>


<dt>MHscale</dt>
<dd>(starting) scale of Metropolis-Hastings update.</dd>


<dt>adapt</dt>
<dd>whether to adapt the scale of the proposal distribution
during burnin to achieve better acceptance rates.</dd>

</dl>

student_t_control: Set (MCMC) computational options for a Student-t likelihood model

Description

Usage

Value

Arguments