Hi all, In the following section: https://graph-tool.skewed.de/static/doc/demos/inference/inference.html#edge-... <https://graph-tool.skewed.de/static/doc/demos/inference/inference.html#edge-weights-and-covariates> Tiago shows how to infer the best model of `foodweb_baywet` between the `real-exponential` model and the `log-normal` model, each being improved by the merge-split algorithm. My question has to do with which *state* one should copy when applying the merge-split algorithm. In the `log-normal` example model, we have: ```python y = g.ep.weight.copy() y.a = log(y.a) state_ln = gt.minimize_nested_blockmodel_dl(g, state_args=dict(recs=[y], rec_types=["real-normal"])) state_ln = state.copy(bs=state_ln.get_bs() + [np.zeros(1)] * 4, sampling=True) for i in range(100): ret = state_ln.multiflip_mcmc_sweep(niter=10, beta=np.inf) -state_ln.entropy() # ~7231 ``` But if I copy the state_ln object instead: ```python state_ln = gt.minimize_nested_blockmodel_dl(g, state_args=dict(recs=[y], rec_types=["real-normal"])) state_ln = *state_ln*.copy(bs=state_ln.get_bs() + [np.zeros(1)] * 4, sampling=True) for i in range(100): ret = state_ln.multiflip_mcmc_sweep(niter=10, beta=np.inf) -state_ln.entropy() # ~4690 ``` There is a big difference between the description length of the two models. My understanding is that the *state* in the first example comes from the previous `real-exponential` model, which means we copy its state then pass the hierarchy levels of the state_ln model. Is it supposed to be so? Shouldn't we always copy the state of the models we ran in the first place to run merge-split algorithm? Jonathan -- Sent from: https://nabble.skewed.de/