I'm working through validating a Bayesian mixture model for multi-species occupancy with a collaborator. Initially, we relied on
coda::heidel.diag to alert us to convergence failures, and after playing more with fitting our model to toy data we are now more skeptical. We fit the same model to many subsets of the real data, and typically
heidel.diag presumes stationarity. What are the pitfalls associated with relying on the Cramer-von-Mises stationarity test, and what additional convergence criteria would be good complements?
have many models to fit, and on fast computers the chains take a couple days to run
can't investigate all trace plots visually... there are too many
- want something stringent enough to deploy automatically, even if it means more careful attention to specific cases that frequently have, in fact, converged....
- are currently worried about interpreting models that haven't converged
- are working through the recent papers mentioned in this post