Comments (4)
from linfa.
Hi, I'm the other JOSS reviewer. I was going to file a similar issue, but I'll put my comments here. I did a quick search for Python packages that implement some form of variational inference and found the following:
- PyMc
- BayesPy (with an accompanying paper, BayesPy: Variational Bayesian Inference in Python)
- Pyro (with some examples)
- PyVBMC has an accompanying JOSS article that includes a "Related work" section, which might be a useful reference?
I also found this notebook. which implements VI from scratch in pytorch
.
Perhaps in the "Capabilities" section, you could add a few remarks to demonstrate how these features of LINFA are more convenient, flexible, powerful, etc, than those found in other packages?
from linfa.
I have added a new paragraph to the paper highlighting the main feature distinguishing LINFA from the packages you guys pointed out. Thanks!
from linfa.
And my apologies if I've misunderstood, but am I right in thinking that the primary distinguishing feature of LINFA is the adaptive training of a surrogate model?
from linfa.
Related Issues (15)
- update call to torch.meshgrid HOT 1
- Contributing guide HOT 1
- reproducibility HOT 1
- Running examples from the test suite HOT 2
- Plotting outputs with linfa.plot_res HOT 1
- Examples in the online documentation HOT 4
- CI test coverage ? HOT 1
- create easy calls for scripts to run test cases and post-process results HOT 1
- finalize tutorial
- table and figure links in sphinx documentation HOT 1
- run test cases from command line HOT 1
- linfa.models is not included in built packages HOT 2
- Missing DOI in the paper HOT 6
- .idea folder HOT 1
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from linfa.