I implemented a a Gibbs Sampler for a hierarchical model with priors and hyperpriors that has around 16 variables.
When it comes to autocorrelation plots, I have seen in some papers that they do not plot all the variables, but it's not clear to me how they chose the subset of variables to plot.
Is there some
good practice about which variables to report (for a paper in a computational statistics journal)?
For instance, my model performs a clustering and has a Dirichlet Process on top. Is this enough to plot the autocorrelation of the concentration parameter of the Dirichlet Process or should I also plot the other variables?