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Why should this notebook be added to pymc-examples?
This comes up a lot on the discourse, most recently here. New users struggle with the API since it's a bit closer to pytensor than the rest of PyMC. There's also some intricacy when one doesn't go the dist kwarg route. For example, you need to know to actually write in numpy/scipy for the random method, but you cannot do that anywhere else in PyMC!
I agree the majority of the notebook should show how to work though the dist argument, but there should at least be a note at the bottom about the other way.
Notebook proposal
Title: Creating Distributions with
CustomDist
Why should this notebook be added to pymc-examples?
This comes up a lot on the discourse, most recently here. New users struggle with the API since it's a bit closer to pytensor than the rest of PyMC. There's also some intricacy when one doesn't go the
dist
kwarg route. For example, you need to know to actually write in numpy/scipy for the random method, but you cannot do that anywhere else in PyMC!Suggested categories:
Related notebooks
Closest existing notebook is the black-box likelihood, but that's even more advanced than what I'm proposing.
References
CustomDist
API docs are already quite nice. I just envision something a bit more hand-holding with more pictures.The text was updated successfully, but these errors were encountered: