Hello. I've got a question about the following example from cookbook https://graph-tool.skewed.de/static/doc/demos/inference/inference.html#id11 I work on my own network in exact same way, trying to perform sampling to estimate some metrics. But the results are in some way replicates the behaviour from the cookbook example: For both cases (simple and nested SBM) the marginal distributions for vertices most of the times has too many non-zero values for different clusters, hence the colouring is so fine granular. Only few (1-2) clusters obey some explicit dominant group membership. But the rest of clusters exhibit very distributed marginals. Do you have any explanation for this? In case of my network I also have only 1-3 groups of nodes with some explicit dominant group membership. And the rest of vertices has too many non-zero, almost uniformly distributed marginals. I was thinking that for the simple cookbook example it's not natural that some vertices has more than 10 non-zero marginal values. May be it's just the result of independent launches of mcmc algorithm and random nature of groups labelling? Or there is some intuition behind this high marginal variance in group membership? I launched several times the optimisation, and drew the results. Topologically the outputs were very close to each other, although colouring was always different except a few kind of "stable" vertices. Hence, I guess, the resulted marginals for them have the same properties. But labels are not informative it selves. May be there is some trick how to force some deterministic labelling policies to stabilise it ? Thank you Valery.