Yeah, from what I can tell there’s nothing out of the ordinary in terms of convergence. There were a total of less than 40 reported divergences out of the 2000 used in the final distribution, but that shouldn’t lead to extremely biased inference right?
Energy Error Distribution:
depth for both chains:
Step Size bar for both chains:
Mean tree accept histogram (mean was 0.8 which matches target_accept given on initialization)
Energy vs energy diff
Further, the Rhat for all parameters was between 0.99 and 1.01 and the minimum effective sample size across all parameters was around 350. The BFMI was 0.9683649816013548, but I’m not sure what this parameter is (I’m assuming it should be near 1).
I’m not sure what the khat of the loo is, but here is the LOO output for the model/trace
LOO_r(LOO=121805.38669244738, LOO_se=626.8930392116976, p_LOO=11.25017988102627, shape_warn=0)
Do you agree with my conclusion about approximate convergence?