I am running a Hidden Markov Model in Stan where some covariates drive the transitions between the latent states. Running the model on larger samples, I am still having issues with poor convergence in most of my parameters (as indicated by Rhat; in another post, it was recommended to apply the non-centered parameterization, which unfortunately didn’t solve the problem).
Looking at the summary output of the 2 chains I was running (each with adapt delta=.90, max_depth=12 num_samples=2000), convergence appears to be quite poor:
Looking at the summary of each chain separately, it looks ways different:
Now the traceplots of the 2 chains show what’s going on. Most of the parameters converge within a chain, but they converge to “slightly” (there is a difference, but this difference does not change the content-related implications I want to draw from this model) different values.
My questions are:
- Am I correct in assuming that running this model again with adjustments to the computational parameters (iterations, adapt_delta, etc.) would do no good at all?
- Is the convergence problem really as big as it seems if it doesn’t change the insights I want to generate with the model?
- Any other recommendations on what I should try?