pymc
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MarginalSparse shouldn't use DensityDist internally
Description of your problem
The MarginalSparse
Gaussian Process uses DensityDist
internally to compute the marginal_likelihood
(here). We should refactor that code to use a proper RandomVariable instead
Versions and main components
- PyMC3 Version: 4.0
- Aesara/Theano Version: 2.2.2
- Python Version: 3.8.5
- Operating system: Ubuntu 18.04
- How did you install PyMC3: (conda/pip) pip
This would be nice to do. It would allow pm.sample_posterior_predictive
to work correctly over the original input domain X
(so, to see the fit) without the need to append an additional gp.conditional
rv. Is there another benefit?
In the interest of keeping #5055 smaller, I think it should be done after that's in, or branched off of it in a separate PR. I might be mistaken, but I think it's more of an enhancement than something broken that must be fixed before v4.
It also makes me wonder if other GP implementations would be better refactored into RandomVariable
s as well.
@bwengals Are you interested in pursuing this one? Or should we add a "help wanted" label?
Closed by #6076