Rahul Mehta

The code you gave me works perfectly. It is when I add: `logprb = proposal.log_prob(theta)` that the error is raised. 2.) Since the process_prior(prior,...) function checks if the prior can...

I was hoping to use the restrictedPrior as a proposal itself in the initialization of the SNPE(prior = RestrictedPrior) (which also checks if the prior has a log_prob) as I...

That is only true for SNPE correct ? For SNLE/SNRE it requires the prior in order to calculate the posterior distribution, P(theta|X) = P(X|theta)P(theta) for SNLE and P(theta|X) = r(X,theta)P(theta)...

Hi Manuel, Your suggestions also work. I just wanted to point out that this happens during the initialization phase, `density_estimator_function = posterior_nn(model="maf", embedding_net=embedding_net, hidden_features=num_hidden, num_transforms=number_of_transforms)` which then calls `build_maf`, which...

Hi Manuel, A deepcopy of the density estimator is also done during training, https://github.com/mackelab/sbi/blob/main/sbi/inference/snpe/snpe_base.py#L424 . Is this also necessary? I don't think a deepcopy of the posterior would be costly...

I commented out the line, https://github.com/mackelab/sbi/blob/main/sbi/inference/snpe/snpe_base.py as it seems to be only used for SNPE-B which has not yet been implemented. The other memory usage comes from storing the simulated...