Saw you mentioning the latent Poisson model on Twitter. I skimmed through what I could understand of the paper. Can it apply to directed graphs? I saw in the paper you were 'erasing' the multiedges back into a simple graph. Can you erase into a simple directed graph? Is it correct to say that this approach supplants degree-correction? And, for DC vs non-DC you had a section in the docs about selecting which one fits your network best given the entropy. Is there something analogous here? Or, do I just try it out and see the results? In the paper you mentioned this can be applied to community detection. As a user, is this as simple as instantiating LatentMultigraphBlockState and then everything else is pretty much the same: equilibrate with the new multiflip, etc? On Twitter you mentioned the latent Poisson approach in relation to link prediction. Over on the other thread you just recommended I use `MeasuredBlockState.get_edge_prob`. What's the difference with `LatentMultigraphBlockStat.get_edge_prob`? Will the latter give better results? I see they're both subclasses of `UncertainBaseState`. Thanks for your help as always