Am 17.10.18 um 23:34 schrieb Tzu-Chi Yen: > 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?
The function minimize_blockmodel_dl() calls many other functions which need
to compute the entropy (among other things), so things are organized in a way
to make the code simpler, and contain the explosion of function parameters,
but it makes options for customization like this a bit hidden. To achieve
what you want, you need to do:
minimize_blockmodel_dl(g,
mcmc_args=dict(entropy_args=dict(degree_dl_kind="uniform")))
Best,
Tiago
--
Tiago de Paula Peixoto <[email protected]>
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