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