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Home Page: https://kulprit.readthedocs.io
License: MIT License
Kullback-Leibler projections for Bayesian model selection in Python
Home Page: https://kulprit.readthedocs.io
License: MIT License
Add two tutorials to the docs:
kulprit
workflow (similar to the main projpred
vignette);Currently only the Gaussian observation family is implemented in kulprit
. We should also allow the use Binomial and Poisson observation families in the reference model.
Allow a user to call ReferenceModel.project()
while passing a tuple of parameter names as opposed to only either a list or an integer.
I don't thing this is a priority at the moment. But at some point in the future, making this change will be beneficial. The two most obvious benefits will be removing one dependency, as aesara is already a Bambi dependency. And avoid duplicating code from bambi.
We could use this issue to track the functions that we actually need tom PyTorch that are currently absent from aesara. And then decided if those should be added upstream or we can add it as part of kulprit. I think we just require a couple of functions or so.
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