Bernoulli graphs can have p>1

[karlrohe]

Bernoulli probabilities can exceed one... because we simulate from poisson and threshold anything above 1.

So, I think this error shouldn't be there.

> b_model = fastRG::sbm(n = 100, k =2, 
+                       B = matrix(c(.8,.4,.4,.8), nrow = 2),  edge_distribution = "bernoulli")
Error: Elements of `B` must be not exceed 1 for bernoulli SBMs.

[alexpghayes]

Maybe we should switch the name of the edge distribution to "approximately-bernoulli" then too? And switch from an error to a warning? Does that sound good to you?

[karlrohe]

In stochastic block models and Erdos-renyi graphs, it is exact. In other models, it is approximate. I’m not sure how to handle this.

…

On Jul 2, 2021, at 10:00 AM, alex hayes ***@***.***> wrote: Maybe we should switch the name of the edge distribution to "approximately-bernoulli" then too? And switch from an error to a warning? Does that sound good to you? — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#20 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AB65UINUARKSXQNUOU6YCR3TVXIARANCNFSM47W23ADA>.

[alexpghayes]

We can do things on a model by model basis. I think it makes sense to leave the error for model where the approximation is exact (i.e. above).

[karlrohe]

But my probabilities weren't greater than one.... it only happens because of the -log(1-p) needed to inflate the Poisson probabilities... so that after thresholding, we get Bernoulli( p)

[karlrohe]

So, the check should go before the -log(1-p)

[alexpghayes]

Ah I should move this code out of the SBM model and into the general factor model constructors.

[alexpghayes]

General solution in #29