I’m sorry if everyone already knows this, but I’m struggling to find information on how to troubleshoot chain mixing where certain parameter draws are moderately autocorrelated, even though the model fits fine - RHats & divergences fine & ESS reasonable even for the problem chains.
Motivation - many models in my field are quite tough to fit because of their heirarchical structure (with many levels at L1) & a requirement for a fair bit of flexibility. Improving chain mixing would help me fit these models faster, as well as reducing the small, but non-negligible probability that the chain finds a nonsensical solution in a different mode.
When I see individual parameter draws mixing poorly but without a restrictive prior my gut feeling is that there’s too much collinearity between variables, & more extreme values of (combinations of) the other collinear variables are moderately restricting the movement in the ‘problem’ dimension). Is this a reasonable intuition? I’d try addressing it by tightening the priors on the other variables, but perhaps I’m talking rubbish here.
What’s your favourite strategy to investigate this type of issue?