Comments (4)
daneschi
commented on July 18, 2024
2
Thank you!
Yes, currently the main differences with the other codes are:
1 - an adaptive discrepancy.
2 - an adaptive annealing algorithm.
Thank you for all your comments. I'm at a conference this week, I'll start
pushing changes related to your comments on Monday.
…On Wed, Feb 28, 2024 at 12:37 AM Rob Moss ***@***.***> wrote:
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?
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robmoss
commented on July 18, 2024
1
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?
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daneschi
commented on July 18, 2024
1
I have added a new paragraph to the paper highlighting the main feature distinguishing LINFA from the packages you guys pointed out. Thanks!
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robmoss
commented on July 18, 2024
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?
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Related Issues (15)
- update call to torch.meshgrid HOT 1
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- linfa.models is not included in built packages HOT 2
- Missing DOI in the paper HOT 6
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