Origin of cutoff point 1000 for a divergence

[jsocolar]

In case it’s relevant, note that Stan saves both the initial point from which a divergent trajectory started (the sample from the previous iteration, provided that we are saving un-thinned samples), and also an additional point sampled from the divergent trajectory (the sample from the divergent iteration), which may or may not be the same as the initial point.

Some potentially relevant discussion here

Getting the location + gradients of divergences (not of iteration starting points)

When I was writing the non-identifiability blog post, I really tried to think hard where divergences are expected to occur. And while the divergences somewhat gathered in the areas I expected them too, they were also often marked quite far from those area. I noticed that the Stan manual says: ShinyStan provides visualizations that highlight the starting point of divergent transitions to diagnose where the divergences arise in parameter space. So this means that if an iteration is divergent, …

and also here

Sampler Diagnostics

When encountering divergences, I often have the problem that these divergences are spread out all over the posterior distribution (or at least the part of it that was already sampled). This makes it difficult to pin down the cause of these divergences. Only in the minority of cases I encountered divergences which are thightly grouped in a certain area, thus giving a hint about what causes them. I think that these divergences are so often spread out is because of two things: The location where…