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smwg.Rmd
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smwg.Rmd
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# <a name='smwg'>Sequential Metropolis-within-Gibbs</a>
(<a name='smwg'>SMWG</a>)
<br>
```{r, echo=FALSE}
pander::pander(
data.frame(
Aspect = c('Acceptance Rate', 'Applications', 'Difficulty', 'Final Algorithm?', 'Proposal'),
Description = c('The optimal acceptance rate is 44%, and is based on the univariate normality of each marginal posterior distribution. The observed acceptance rate may be suitable in the interval [15%,50%].', 'This algorithm is applicable with state-space models (SSMs), including dynamic linear models (DLMs).','This algorithm is relatively easy for a beginner when the proposal variance has been tuned with the SAMWG algorithm. Otherwise, it may be tedious for the user to tune the proposal variance.','Yes.', 'Componentwise.')),
justify='left', style='multiline', split.cells=c(17,83), split.tables=Inf)
```
<br>
The Sequential Metropolis-within-Gibbs (SMWG) algorithm is the non-adaptive version of the Sequential Adaptive Metropolis-within-Gibbs (SAMWG) algorithm, and is used for final sampling of state-space models (SSMs).
## References
- none
## See Also
- [SAMWG](#samwg)