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"""A distribution that can be used to wrap black-box log density functions.
Creates a Distribution and registers the supplied log density function to be used
for inference. It is also possible to supply a `random` method in order to be able
to sample from the prior or posterior predictive distributions.
"""
We should also add a warning, similar to the Interval transform, stating the logp / logcdf / random / get_moment functions cannot rely on nonlocal variables, but only on constants + distribution parameters:
Also, the __new__ method should have no docstring. All the content there should be in the class docstring, otherwise it doesn't appear in the documentation
Also, the __new__ method should have no docstring. All the content there should be in the class docstring, otherwise it doesn't appear in the documentation
We should include code snippets about how to use the DensityDist, as 1) the API has changed since V3, and 2) it has always been a source of confusion
pymc/pymc/distributions/distribution.py
Lines 680 to 686 in 7a6bd02
We should also add a warning, similar to the Interval transform, stating the logp / logcdf / random / get_moment functions cannot rely on nonlocal variables, but only on constants + distribution parameters:
pymc/pymc/distributions/transforms.py
Lines 204 to 207 in 7a6bd02
See #5614 and related issue for more context on this limitation
Bonus points for including an example of a Multivariate distribution and explaining
ndim_supp
andndim_params
Not everything has to be done at once or by the same person!
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