How to interpret rank bar plot of a MCMC trace? I am learning how to use PyMC for Bayesian inference. I coded up a random intercept $Y = \gamma + \sum_{j=1}^3 \beta_j \mathbb{I}_j + \epsilon$ and looked at the trace plots. Here is a graphviz visualization of the model's structure.

Out of curiosity I tried kind="rank_bars" in arviz.plot_trace. Here is the output.

And here is a closer look at the beta params' rank bar plot.

What diagnostic information about the MCMC simulation do these plots tell me about the simulation? What would it look like if something was working/not working?

From the dox:

From the paper: Rank plots are histograms of the ranked posterior
draws (ranked over all chains) plotted separately for each chain. If
all of the chains are targeting the same posterior, we expect the
ranks in each chain to be uniform, whereas if one chain has a
different location or scale parameter, this will be reflected in the
deviation from uniformity. If rank plots of all chains look similar,
this indicates good mixing of the chains.
This plot was introduced by Aki Vehtari, Andrew Gelman, Daniel
Simpson, Bob Carpenter, Paul-Christian Burkner (2019):
Rank-normalization, folding, and localization: An improved R-hat for
assessing convergence of MCMC. arXiv preprint
https://arxiv.org/abs/1903.08008

