Dear Tiago, I would like to fit an SBM with the /minimize_blockmodel_dl()/ function. Specifically, I would like to customize the optimization procedure with different priors for the model parameters. I am aware that /BlockState.entropy()/ returns the entropy (for fitting to SBM) with *labelled* input (partition & degree sequence), and /model_entropy()/ returns the entropy (for constructing the model) with *static* input (B, N, E). However, I don't see an argument in the /minimize_blockmodel_dl()/ function that I could enforce certain parameter priors at the first place, be it /degree_dl_kind == "uniform"/ or /degree_dl_kind == "distributed"/.
Do I miss something from the documentation? For example, may I customize /state_args/ in /minimize_blockmodel_dl()/ for this purpose? Sincerely thanks, Tzu-Chi -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/ _______________________________________________ graph-tool mailing list [email protected] https://lists.skewed.de/mailman/listinfo/graph-tool
